News Article | May 9, 2017
A new study, published in Philosophical Transactions of the Royal Society B, found that some types of conservation action could increase the abundance of ticks, which transmit diseases like Lyme disease. The research – led by the University of Glasgow in collaboration with Scottish Natural Heritage, the James Hutton Institute and Public Health England – examined how conservation management activities could affect tick populations, wildlife host communities, the transmission of the Borrelia bacteria that can cause Lyme disease and, ultimately, the risk of contracting Lyme disease. The study found that managing the environment for conservation and biodiversity has many positive effects, including benefits for human health and wellbeing from spending time in nature; however the researchers suggested that there should be consideration of disease vectors such as ticks and mosquitoes in conservation management decisions. Lead author Dr Caroline Millins, from the University of Glasgow's School of Veterinary Medicine and Institute of Biodiversity, Animal Health and Comparative Medicine (BAHCM), said: "We identified several widespread conservation management practices which could affect Lyme disease risk: the management of deer populations, woodland regeneration, urban greening and control of invasive species. "We found that some management activities could lead to an increased risk of Lyme disease by increasing the habitat available for wildlife hosts and the tick vector. These activities were woodland regeneration and biodiversity policies which increase the amount of forest bordering open areas as well as urban greening. "However, if deer populations are managed alongside woodland regeneration projects, this can reduce tick populations and the risk of Lyme disease." Deer are often key to maintaining tick populations, but do not become infected with the bacteria. Previous research by co-author Lucy Gilbert of The James Hutton Institute has shown that greatly reducing deer densities by exclusion fencing or culling can reduce tick density and therefore Lyme disease risk. Senior author Dr Roman Biek, University of Glasgow's BAHCM, said: "Widespread management activities can potentially teach us a lot about how changes to the environment can affect the chances of humans coming into contact with ticks and with the pathogens ticks transmit. We recommend that monitoring ticks and pathogens should accompany conservation measures such as woodland regeneration and urban greening projects. This will allow appropriate guidelines and mitigation strategies to be developed, while also helping us to better understand the processes leading to higher Lyme disease risk." Co-author Professor Des Thompson, Principal Adviser on Science and Biodiversity with Scottish Natural Heritage, commented: "This is the sort of vital research we need to act on in order to advise Government on the best practices for enhancing wildlife whilst minimising risks to human health. The Scottish Government's 2020 plan for Scotland's Biodiversity requires this integrated approach, bringing human health and wildlife management sectors together." Explore further: Lyme disease researchers seek consensus as number of cases grows More information: Caroline Millins et al. Effects of conservation management of landscapes and vertebrate communities on Lyme borreliosis risk in the United Kingdom, Philosophical Transactions of the Royal Society B: Biological Sciences (2017). DOI: 10.1098/rstb.2016.0123
News Article | May 11, 2017
Whole genome sequencing (WGS), which is the process of determining an organism's complete DNA sequence, can be used to identify DNA anomalies that cause disease. Identifying disease-causing DNA abnormalities allows clinicians to better predict an effective course of treatment for the patient. Now, in a series of recent studies, scientists at the University of Missouri are using whole genome sequencing through the 99 Lives Cat Genome Sequencing Consortium to identify genetic variants that cause rare diseases, such as progressive retinal atrophy and Niemann-Pick type 1, a fatal disorder in domestic cats. Findings from the study could help feline preservationists implement breeding strategies in captivity for rare and endangered species such as the African black-footed cat. The 99 Lives project was established at Mizzou by Leslie Lyons, the Gilbreath-McLorn Endowed Professor of Comparative Medicine in the College of Veterinary Medicine, to improve health care for cats through research. The database has genetically sequenced more than 50 felines and includes DNA from cats with and without known genetic health problems. The goal of the database is to identify DNA that causes genetic disorders and have a better understanding of how to treat diseases. In the first study, Lyons and her team used the 99 Lives consortium to identify a genetic mutation that causes blindness in the African black-footed cat, an endangered species often found in U.S. zoos. The team sequenced three cats ? two unaffected parents and an affected offspring ? to determine if the mutation was inherited or spontaneous. The genetic mutation identified was located the IQCB1 gene and is associated with progressive retinal atrophy, an inherited degenerative retinal disorder that leads to blindness. The affected cat had two copies of the genetic mutation, indicating that it was an inherited disorder. "African black-footed cats are closely related to domestic cats, so it was a good opportunity to use the 99 Lives database," Lyons said. "When sequencing DNA, we are looking for the high priority variants, or genetic mutations that result in disease. Variants in the IQCB1 gene are known to cause retinal degeneration in humans. We evaluated each gene of the African black-footed cat, one at a time, to look for the genetic mutation that is associated with vision loss." In another study representing the first time precision medicine has been applied to feline health, Lyons and her team used whole genome sequencing and the 99 Lives consortium to identify a lysosomal disorder in a 36-week-old silver tabby kitten that was referred to the MU Veterinary Health Center. The kitten was found to have two copies of a mutation in the NPC1 gene, which causes Niemman-Pick type 1, a fatal disorder. The NCP1 gene identified is not a known variant in humans; it is a rare mutation to the feline population. "Genetics of the patient is a critical aspect of an individual's health care for some diseases," Lyons said. "Continued collaboration with geneticists and veterinarians could lead to the rapid discovery of undiagnosed genetic conditions in cats. The goal of genetic testing is to identify disease early, so that effective and proactive treatment can be administered to patients." Identification of both the IQCB1 gene in the African black-footed cat and the NCP1 in the silver tabby will help to diagnose other cats and allow them to receive appropriate treatment. Using results of the black-footed cat study, zookeepers will be implementing species survival plans to help manage the cats in captivity in North America. The study, "Early-Onset Progressive Retina Atrophy Associated with an IQCB1 Variant in the African Black-Footed Cates (Felis nigripes)," recently was published in Scientific Reports. Funding was provided by the University of Missouri, College of Veterinary Medicine Clinician Scientific Grant. The study, "Precision Medicine in Cats: Novel Niemann-Pick Type C1 Diagnosed by Whole-Genome sequencing," recently was published in the Journal of Veterinary Internal Medicine.
News Article | May 17, 2017
Previously published KrasLSL-G12D (ref. 28), Trp53flox/flox (ref. 29), KrasFSF-G12D (ref. 30), Trp53frt/frt (ref. 31), Rosa26LSL-tdTomato (ref. 32), Apcflox/flox (ref. 33), Rosa26LSL-luciferase (ref. 34), Rosa26mTmG (ref. 35), Lgr5GFP-IRES-CreER/+ (ref. 36) and Lgr5CreER/+ (ref. 8) gene-targeted mice were used in the study. All mice were maintained in a mixed Sv129/C57BL/6 genetic background. Tumours were induced in KP mice with 2.5 × 107 plaque-forming units (PFU) of AdCMV-Cre (Iowa), 2 × 108 PFU of AdSPC-Cre23, 37, 1 × 108 PFU of AdCMV-FlpO (Iowa) or 15–50,000 transforming units of lentiviral Cre, as previously described38, 39, in mice that were between 8–12 weeks of age. Approximately equal numbers of male and female mice were included in all experimental groups in all mouse experiments. Mice bearing lung tumours were treated with 10 mg per kg per day of LGK974 (ref. 20) resuspended in 0.5% carboxymethylcellulose (Sigma-Aldrich) and 0.5% Tween 80 (Sigma-Aldrich) or vehicle (0.5% carboxymethylcellulose and 0.5% Tween 80 only). Weights of mice were followed weekly. The growth of autochthonous KrasG12D/+;Trp53Δ/Δ;Rosa26Luciferase/+ lung tumours was followed longitudinally by bioluminescence imaging, as previously described34. In brief, mice were anaesthetized by isoflurane inhalation, administered 100 mg kg−1 d-luciferin (Perkin Elmer) by intraperitoneal injection and imaged after 10 min, using the IVIS imaging system (Perkin Elmer). Such longitudinal imaging experiments were repeated three times and representative data from one such experiment is shown in Fig. 4a. Survival experiments were repeated three times and representative data from one such experiment is shown in Fig. 4b. For survival experiments, mice were randomized based on their tumour burden as assessed by μCT. Mice were assigned a tumour burden score ranging from 0 (no tumours) to 10 (lungs completely full of tumours), and experimental groups were formed such that each group had approximately equal average tumour burdens. Mice with tumour burden scores under 3 were excluded from the study. The health of the mice in all experiments was monitored daily by the investigators and/or veterinary staff at the Department of Comparative Medicine at Massachusetts Institute of Technology. Mice with a body condition score under 2 were humanely euthanized. Animal studies were approved by the Massachusetts Institute of Technology (MIT) Committee for Animal Care (institutional animal welfare assurance no. A-3125-01). The maximal tumour dimensions permitted by the MIT Committee for Animal Care were 2 cm across the largest tumour diameter and this limit was not reached in any of the experiments. Mice bearing KrasG12D/+;Trp53Δ/Δ;Rosa26tdTomato/+ (KPT) or KrasG12D/+;Trp53Δ/Δ;Rosa26tdTomato/+;Lgr5GFP-CreER/+(KPT;Lgr5GFP-CreER/+) LUAD tumours were euthanized 12–26 weeks after tumour induction and perfused with S-MEM (Gibco) through the right ventricle of the heart. Dissected lungs with tumours were dissociated in protease and DNase solution of the Lung Dissociation kit (Miltenyi Biotech) followed by mechanical dissociation using MACS C columns (Miltenyi Biotech) according to the manufacturer’s instructions. The dissociated cells were filtered using a 100-μm strainer and red blood cells were lysed using ACK (Thermo Scientific), followed by staining with APC-conjugated CD31 (Biolegend, 102510), CD45 (BD, 559864), CD11b (eBioscience, 17-0112-82) and TER119 (BD, 557909) antibodies and dead cells with DAPI (Sigma-Aldrich). The same approach using the Tumour Dissociation kit (Miltenyi Biotech) was used to isolate KPT;Lgr5GFP-CreER/+;Pdx1::Cre PDAC tumours cells when mice were 7 weeks of age. Fluorescence-activated cell sorting (FACS) of stained primary cells was performed using a FACSAria sorter (BD) by gating for tdTomato+/DAPI−/APC− cells (total cancer cell fraction) for KPT tumours. For KPT;Lgr5GFP-CreER/+ tumours, both tdTomato+/DAPI−/APC−/GFP+ (Lgr5+ cancer cell fraction) and tdTomato+/DAPI−/APC−/GFP− (Lgr5− cancer cell fraction) populations were sorted. Sorted cells were placed in 3D organotypic culture, transplanted intratracheally into NOD/SCID-γ (NSG) recipient mice, or subcutaneously into athymic nu/nu mice immediately after sorting (see below). For intratracheal transplantation, 8–10-weeks-old immunodeficient NSG mice were anaesthetized, intubated as previously described38, and allowed to inhale 15–50,000 sorted primary KP LUAD cancer cells resuspended in 30 μl of S-MEM (Gibco). For subcutaneous transplantation, 50–500,000 sorted primary KP LUAD cells, KP LUAD cell lines or single-cell clones derived from a KP;Lgr5GFP-CreER/+ LUAD cell line were resuspended in 50% Matrigel/50% S-MEM and injected subcutaneously into both flanks of athymic nu/nu mice in a volume of 100 μl. Mice with transplant tumours were injected intraperitoneally with 1 mg of 5-ethynyl-2-deoxyuridine (EdU, Setareh Biotech) 4 h before euthanasia to label proliferating cells. EdU was detected in cryosections using the Click-iT EdU Alexa Fluor 488 Imaging kit (Thermo Scientific) according to the manufacturer’s protocol. Lgr5+ cells in close proximity to porcupine were detected by GFP and porcupine immunofluorescence. All GFP+ cells were analysed as being immediately adjacent to at least one porcupine+ cell, as double-positive for both GFP and porcupine, or as neither of the above (Fig. 3a). All transplantation experiments were reproduced three times. 150–1,000 primary mouse KP LUAD cells, cells from established KP LUAD cell lines, or primary mouse PDAC cells were mixed in 50% Matrigel (BD) and 50% advanced DMEM/F12 (Gibco) and plated on 10 μl of Matrigel. The gel was allowed to solidify at 37 C, followed by addition of advanced DMEM/F12 (Thermo Scientific) supplemented with gentamicin (Thermo Scientific), penicillin–streptomycin (VWR), 10 mm HEPES (Thermo Scientific) and 2% heat-inactivated fetal bovine serum. For Wnt pathway manipulation, cultures were incubated with 1 μg ml−1 recombinant mouse (rm)R-spondin 1 (Sino Biological), 100 ng ml−1 rmWnt3a (R&D Sytstems), 500 ng ml−1 or 1 μg ml−1 rmDKK1 (R&D Systems) or 100 nM LGK974 (Medchem Express) for 6–14 days. Medium was changed every two days. At the end of the experiment, proliferating cells were labelled with 10 μM EdU for 4 h, followed by paraformaldehyde fixation and fluorescent labelling of proliferating cells using the Click-iT EdU Alexa Fluor 488 Imaging kit (Thermo Scientific), according to the manufacturer’s protocol, in whole-mount preparations of tumour spheroids. Proliferating spheroids were quantified using a Nikon Eclipse 80i microscope: a spheroid was classified as a cluster of at least 10 cells, and a proliferating spheroid contained at least one EdU positive nucleus (proliferating cells were not observed in clusters of cells smaller than 10 cells). At least four replicate wells per condition were quantified in each experiment. Images were acquired using a Nikon A1R confocal microscope. Stimulation and inhibitor experiments were reproduced at least 10 times for each experimental condition. Multiple cell lines were established from the mouse LUAD and PDAC KP GEMMs over the course of the study. The cell lines have not been authenticated. The cell lines were routinely tested for mycoplasma and found to be negative. At the time of conducting the experiments, no cell lines used were found to be listed in the ICLAC database of misidentified cell lines. Tissues or tumour organoids were fixed in 10% formalin overnight and embedded in paraffin. Immunohistochemistry (IHC) was performed on a Thermo Autostainer 360 with or without haematoxylin counterstaining using antibodies against β-catenin (BD, 610153), Ki67 (Vector Labs, VP-RM04), glutamine synthetase (BD, 610517), or porcupine (Abcam, ab105543). Lungs from at least three tumour-bearing mice were analysed for each antibody. Livers and small intestines collected from three normal, healthy mice were used for β-catenin, glutamine synthetase and porcupine IHC. 65 human LUAD tumours samples in two separate tissue microarrays were analysed by IHC for β-catenin and porcupine. 5 human colorectal adenocarcinoma samples were stained with porcupine antibodies. All human tissue material was obtained commercially from Janssen Pharmaceuticals. Mice were anaesthetized and perfused through the right cardiac ventricle with 1% paraformaldehyde. Lungs with tumours were dissected, immersed in 4% PFA overnight and frozen in OCT medium (Sakura Finetek). 7 μm sections were stained with antibodies to EpCAM (eBioscience, 17-5791-82), β-catenin (BD, 610153), GFP (Cell Signaling Technologies, 2956S; or Aves Labs, GFP-1020), CD11b (eBioscience, 17-0112-82) or porcupine (Abcam, ab105543). Lungs from at least three tumour-bearing mice were analysed for each antibody. Digitally scanned images of Ki67-stained slides were created with the Aperio ScanScope AT2 at 20× magnification. Aperio’s WebScope software was used to assess Ki67+ density per tumour area. A built-in IHC nuclear image analysis algorithm was used to classify cells on the basis of the intensity of the nuclear Ki67 stain. Nuclei were classified from 0 to 3+; only nuclei with moderate nuclear staining (2+) or intense nuclear staining (3+) were considered Ki67+. Tumour regions were outlined on WebScope before running the IHC nuclear image analysis algorithm such that the number of 2+ and 3+ cells was normalized to tumour area. Total RNA was isolated from tumours or cells using the RNeasy plus kit (Qiagen) according to the manufacturer’s instructions. cDNA was synthesized from 1 μg of RNA using the SuperScript VILO cDNA synthesis kit (Thermo Scientific). qPCR was performed in triplicates with 2 μl of diluted cDNA (1:10) using PerfeCTa SYBR Green FastMix (Thermo Scientific) on a Bio-Rad iCycler RT–PCR detection system. Expression was normalized to Actb or Gapdh. All oligonucleotides used in this study are listed in Supplementary Table 4. All qPCR experiments were reproduced using at least three biological replicates. Alternatively, a Mouse WNT Signalling Pathway RT2 Profiler PCR Array (Qiagen) was used according to the manufacturer’s instructions. Raw expression values were thresholded to remove genes that were not detected or had low expression (maximum C value set to 33; 0 values set to 33). Array position to gene-name mapping details were retrieved from the manufacturer’s website (www.pcrdataanalysis.sabiosciences.com). Expression values for all genes per array were normalized to the expression of the housekeeping gene Gusb. Three replicates of stroma samples and three replicates of tumour samples were compared to calculate log fold change and differential expression significance values (two-sided t-test). shRNAs were cloned into lentiviral pLKO.1 vectors (Addgene, 10878) or into pTRIPZ (Dharmacon) vectors and lentivirus was produced as previously described40. KP mouse LUAD cell lines were infected with the lentiviral vectors, followed by puromycin selection and, in the case of cells infected with the TRIPZ virus, incubation in 1 μg ml−1 doxycycline for four days and RNA extraction for testing target knockdown (Extended Data Fig. 2a and not shown). For combined Lgr4 and Lgr5 silencing experiments, cell lines expressing pLKO.1 driving Lgr4 or Lgr5 shRNAs were generated by puromycin selection, followed by infection with TRIPZ vectors driving miR30-based Lgr4 or Lgr5 shRNAs and turboRFP under the control of a TET-responsive promoter. Cells were incubated in 1 μg ml−1 doxycycline for two days and red fluorescent cells were sorted to generate pure cell lines expressing combinations of Lgr4 and Lgr5 shRNAs. All shRNA experiments were reproduced using at least three independent cell lines. 10,000 of KP LUAD cells were plated in 100 μl of medium containing 10% FBS per well of a white-walled 96-well plate (Perkin Elmer). After 24 h, mouse KP LUAD cells were transfected using Attractene transfection reagent (Qiagen) according to the manufacturer’s instructions with 150 ng of the TOPFLASH Firefly (M50) reporter41 (Addgene, 12456) and 20 ng of pRL-SV40P Renilla (Addgene, 27163) constructs. In initial experiments, the Wnt-insensitive FOPFLASH (negative control) Firefly (M51) reporter41 (Addgene, 12457) was used to rule out signal background (not shown). Cells were stimulated for 16 h with recombinant Rspo1 (1 μg ml−1, Sino Biological), recombinant Wnt3a (100 ng ml−1, R&D Systems) or their combination (RW) in advanced DMEM/F12 (Gibco), with supplements listed above. After stimulation, Firefly and Renilla signals were detected using Dual-Glo luciferase detection reagents (Promega) according to the manufacturer’s instructions. A Tecan Infiniti 200 Pro plate reader and automated injector system was used to detect luminescence. To control for transfection efficiency, Firefly luciferase levels were normalized to Renilla luciferase levels to generate a measurement of relative luciferase units. Experimental data are presented as mean ± s.d. from three independent wells. All TOPFLASH experiments were reproduced using at least three independent cell lines. Catalytically dead Cas9 (dCas9)-based systems have recently emerged as powerful tools for transcriptionally activating endogenous genes42. Notably, these systems allow for overexpression of genes in their endogenous genomic context. To overexpress Rspo2, Rspo3 or Lgr5 in KrasG12D/+;Trp53Δ/Δ LUAD cell lines, we used the SAM system, which is a three-component system based on: (1) the fusion of dCas9 to the transcriptional activator VP64 (a tandem repeat of four DALDDFDLDML sequences from Herpes simplex viral protein 16, VP16); (2) a modified gRNA scaffold containing two MS2 RNA aptamers; and (3) the MS2–p65–HSF1 tripartite synthetic transcriptional activator21. In this system, sgRNA-dependent recruitment of dCas9–VP64 and MS2–P65–HSF1 to the endogenous Rspo2, Rspo3 or Lgr5 loci results in potent transcriptional activation (Extended Data Fig. 1i–l). Non-clonal KrasG12D/+;Trp53Δ/Δ;Rosa26tdTomato/+ or KrasG12D/+;Trp53Δ/Δ;Lgr5GFP-CreER/+ LUAD cells stably expressing dCas9–VP64–blast (Addgene, 61425) and MS2–P65–HSF1–hygro (Addgene, 61426) were generated using sequential lentiviral transduction and selection with blasticidin and hygromycin, respectively. To overexpress Rspo2 or Rspo3 we designed four independent sgRNA sequences targeting the Rspo2 or Rspo3 transcription start site; sgRNAs targeting the upstream region of the Lgr5 gene were provided by L. Gilbert, M. Horlbeck and J. Weissman43. The sgRNAs were cloned into a lentiviral vector (Lenti-sgRNA-MS2-zeocin; Addgene, 61427) followed by transduction and zeocin selection of the aforementioned cell lines to generate KrasG12D/+;Trp53Δ/Δ;Lgr5GFP-CreER/+ LUAD cell lines stably expressing all three components. These experiments were reproduced using three independent cell lines. The 7TCF::luciferase-PGK::Cre, 7TCF::GFP-PGK-Cre and U6::sgRNA-EFS::Cre (pUSEC) lentivirus vectors were generated by Gibson assembly44, 45. In brief, a 1.8-kb part corresponding to 7TCF::luciferase or a 1.2-kb part corresponding to 7TCF::GFP were amplified from 7TFP (Addgene, 24308, ref. 46) or 7TGP (Addgene, 24305, ref. 46) respectively, and fused with a 0.5-kb PGK promoter part, a 1.0-kb Cre cDNA part and the PmeI and BsrGI linearized LV1-5 (Addgene, 68411) part44. U6::sgRNA-EFS::Cre was generated by amplifying a 2.2-kb part corresponding to the U6-filler-chimeric gRNA backbone from pSECC (Addgene, 60820), and fused with a 0.25-kb EFS promoter part, a 1.0-kb Cre cDNA part and the PmeI and BsrGI linearized LV1-5 (Addgene, 68411) part44. Lentivirus was produced in 293FS* cells, as previously described38. Experiments using 7TCF::luciferase-PGK::Cre (Fig. 2a) were performed twice (n = 15 mice in total) and experiments using 7TCF::GFP-PGK-Cre (Fig. 2b) three times (n = 19 mice in total). For generation of lentiviruses containing sgRNAs, three sgRNAs per gene targeting Porcn, Lgr4 or Lgr5 were designed using CRISPR Design47, cloned into pSpCas9(BB)-2A-GFP (pX458, Addgene, 48138) as previously described48, transfected into KP cells49, and screened for efficiency by western blotting for porcupine protein or by massively parallel sequencing of the regions in Lgr4 or Lgr5 targeted by the respective sgRNAs (data not shown). The most efficient Porcn sgRNA was cloned into pSECC as previously described49. The most efficient Lgr4 and Lgr5 sgRNAs were cloned into the pUSEC vector together with a synthetic mouse/human U6 promoter (sU6), as previously described50, to generate U6::sgLgr4-sU6::Lgr5-EFS::Cre (pU2SEC). A KPT LUAD cell line was transduced with 7TCF::luciferase-PGK::Puro (7TFP) lentiviruses46, selected for puromycin resistance, and transplanted subcutaneously into flanks of immunodeficient athymic nu/nu mice. Three weeks after transplantation, tumour burden was measured by registering tdTomato fluorescence using an IVIS imaging system (Perkin Elmer), followed by administration of 100 mg kg−1 d-Luciferin (Perkin Elmer) and detection of the luciferase signal (7TCF promoter activity). The luciferase signal was normalized to the tdTomato signal (Wnt pathway activity/total tumour burden). Quantification of Wnt pathway activity was performed every 24 h for a week in mice treated with 10 mg per kg per day of LGK974 or vehicle. The maximal tumour dimensions permitted by the MIT IACUC were 2 cm across the largest tumour diameter and this limit was not reached in these experiments. This experiment was performed twice. Single-molecule in situ hybridization was performed on formalin-fixed paraffin-embedded tissues using the Advanced Cell Diagnostics RNAscope 2.5 HD Detection kit (322360). Catalogue numbers of the probes are 400331 (Axin2), 318321 (Lgr4), 312171 (Lgr5) and 404971 (Porcn), 401991 (Rspo1), 316791 (Wnt5a), 401121 (Wnt7a) and 401131 (Wnt7b). Lungs from three tumour-bearing mice were analysed. We generated KrasFSF-G12D/+;Trp53frt/frt;Lgr5CreER/+;Rosa26mTmG/+ mice and induced lung tumours by intratracheal administration of AdCMV-FlpO. Lung tumours were collected, enzymatically dissociated and passaged in vitro for 8–10 passages to eradicate stromal cells from the cultures. Such early-passage cell lines were transplanted subcutaneously into flanks of NSG mice. When mice developed palpable tumours, they were administered a single tamoxifen pulse (20 mg kg−1) or corn-oil vehicle control. Tumours were collected at 2 days or 14 days after tamoxifen administration and prepared for cryosectioning. Three sections 500 μm apart were prepared from each tumour and imaged under a fluorescence microscope. The number of GFP+ cells per section was quantified in nine tumours per time point. An eXplore CT 120 microcomputed tomography (μCT) system (Northridge Tri-Modality Imaging Inc.) was used for in vivo imaging. Mice were imaged under anaesthesia (induced at 3% isoflurane in oxygen, maintained at between 2–2.5% during imaging) in groups of 4 in a custom mouse holder. Scanner settings were as follows: 720 views, 360° rotation, 70 kVp, 50 mA, 32 ms integration time with 2 × 2 detector pixel binning (isotropic nominal resolution of 50 μm). Data were reconstructed using the Parallax Innovations GPU accelerated reconstruction engine for the eXplore CT120. Tissue density values (in Hounsfield Units (HU)) for normal, air-filled lung parenchyma were determined by eye using MicroView software (Parallax Innovations). For the scanning conditions in this study a range of −550 to −300 HU was determined to represent the range of normal lung parenchyma values. A custom analysis script was created using MATLAB (MathWorks) to identify a region of interest (ROI) including the soft tissue of the mouse thorax. Within this region the volume of tissue within the ‘healthy’ density range was measured. Within this same volume minimum intensity projections (MinP) were created, both to confirm the accuracy of the ROI and to qualitatively assess lung pathology. For data visualization, the change in healthy lung volume was inverted to represent the change in tumour volume (Extended Data Fig. 10b). One experiment involving 9 mice treated with LGK974 and 11 mice treated with vehicle control was carried out to track changes in tumour volume (Extended Data Fig. 10b). RNA-seq gene expression profiles of primary tumours and relevant clinical data of 488 patients with lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA LUAD; http://cancergenome.nih.gov/). The previously published Wnt signalling geneset22 (24 genes upregulated after stimulation with recombinant human WNT3A) was obtained from the Molecular Signatures Database (MSigDB)51 and used to score individual patient expression profiles using ssGSEA52, 53. Patients were stratified according to their correlation score, into top (n = 115) and bottom (n = 114) 20th percentile sets. Kaplan–Meier survival analysis was conducted between these sets of patients and the log-rank test was used to assess significance. Subsequently, the Kaplan–Meier survival analysis methodology was extended to assess significant survival differences across 35 TCGA cancer types using a similar strategy. Additionally, the Cox proportional hazards regression model was used to analyse the prognostic value of the published geneset22 across all patients within the TCGA LUAD cohort, in the context of additional clinical covariates. All univariate and multivariable analyses were conducted within a five-year survival timeframe. The following patient and tumour-stage clinical characteristics were used: signature (signature from ref. 22 strong versus weak correlation); gender (male or female); age (years, continuous); smoking history (reformed >15 years versus non-smoker, reformed <15 years versus non-smoker, current smoker versus non-smoker); mutational load (derived as the number of non-silent mutations per 30 Mb of coding sequence, continuous); Union for International Cancer Control (UICC) TNM Stage specification (stage III/IV versus I/II); UICC T score specification (T2 versus T1, T3/T4 versus T1); UICC N score specification (N1/N2 versus N0). Hazard ratio proportionality assumptions for the Cox regression model were validated by testing for all interactions simultaneously (P = 0.703). Interaction between the signature of ref. 22 and TNM stage, T score and N score (significant covariates in the model) were tested using a likelihood ratio test to contrast a model consisting of both covariates with another model consisting of both covariates plus an interaction term. No statistically significant difference was found between the two models (TNM, P = 0.8751; T score, P = 0.8204; N score: P = 0.8625; likelihood ratio test). To test for statistically significant differences between the previously published22 signature correlation scores across TCGA LUAD grade levels (T scores), the Kurskal–Wallis test was used to assess overall significance and the Mann–Whitney–Wilcoxon test was used to assess pairwise differences. All statistical analyses were conducted in R (http://www.R-project.org) and all survival analyses and were conducted using the survival package in R. Finally, we analysed the expression of Wnt pathway genes present in the Mouse WNT Signalling Pathway RT2. Profiler PCR Array (Qiagen) in the human TCGA LUAD data (Supplementary Table 3). Expression levels between 57 LUAD tumour samples and corresponding matched normal samples were analysed using empirical cumulative distribution function plots. Significance of different expression levels was assessed using the Kolmogorov–Smirnov test. For a more comprehensive analysis covering human orthologues of all WNT pathway genes tested on the mouse qPCR array, pairwise differential expression analysis (tumour versus normal, n = 57 each) was performed using EBSeq version 1.4.0 (ref. 54). CRISPR-Cas9-induced mutations were detected as before59. Briefly, genomic regions containing the sgPorcn, sgLgr4 or sgLgr5 target sequences were amplified using Herculase II Fusion DNA polymerase and gel purified (primer sequences are shown in Supplementary Table 4). Sequencing libraries were prepared from 50 ng of PCR product using the Nextera DNA Sample Preparation kit (Illumina) according to the manufacturer’s instructions and sequenced on Illumina MiSeq sequencers to generate 150-bp, paired-end reads. CRISPR-Cas9-mutated loci were computationally analysed as before59. Briefly, illumina MiSeq reads (150 bp paired-end) were trimmed to 120 bp after reviewing base quality profiles, in order to remove lower quality 3′ ends. Traces of Nextera adapters were clipped using the FASTX toolkit (Hannon Laboratory, CSHL) and pairs with each read greater than 15 bp in length were retained. Additionally, read pairs where either read had 50% or more bases below a base quality threshold of Q30 (Sanger) were removed from subsequent analyses. The reference sequence of the target locus was supplemented with 10 bp genomic flanks and was indexed using an enhanced suffix array55. Read ends were anchored in the reference sequence using 10 bp terminal segments for a suffix array index lookup to search for exact matches. A sliding window of unit step size and a maximal soft-clip limit of 10 bp were used to search for possible anchors at either end of each read. For each read, optimal Smith–Waterman dynamic programming alignment56 was performed between the reduced state space of the read sequence and the corresponding reference sequence spanning the maximally distanced anchor locations. Scoring parameters were selected to allow sensitive detection of short and long insertions and deletions while allowing for up to four mismatches and the highest scoring alignment was selected. Read pairs with both reads aligned in the proper orientation were processed to summarize the number of wild-type reads and the location and size of each insertion and deletion event. Overlapping reads within pairs were both required to support the event if they overlapped across the event location. Additionally, mutation events and wild-type reads were summarized within the extents of the sgRNA sequence and PAM site by considering read alignments that had a minimum of 20 bp overlap with this region. Mutation calls were translated to genomic coordinates and subsequently annotated using Annovar57. The alignment and post-processing code was implemented in C++ along with library functions from SeqAn58 and SSW59 and utility functions in Perl and R (http://www.R-project.org). Mutation calls were subjected to manual review using the Integrated Genomics Viewer (IGV)60. Statistical analysis was carried out as indicated in the Figure legends, Extended Data Figure legends and in the Methods for each experiment. The data were found to meet the assumptions of the statistical tests. Variation was estimated for each group of data, the variance was found to be similar between the groups that were compared. No animals were excluded from any of the studies. The investigator was blinded with respect to group assignment for the quantification of 3D spheroids, proliferating (Ki67+) cells and for the analysis of healthy lung volume by μCT. Power calculations were performed to estimate the sample size for experiments involving LGK974 treatment. In brief, to detect a difference of 30% in average survival between the two groups (effect size = 1.2 s.d. of survival based on Cohen’s d (ref. 61) using untreated sample baseline survival from ref. 39) with 90% power, a minimum of five mice per group needed to be used. Massively parallel sequencing data are available in the NCBI/SRA data repository under accession number PRJNA379539. Source code and all other data are available from the authors upon reasonable request.
News Article | May 26, 2017
A new nanodiamond-based contrast agent—a chemical “dye” that enhances the visibility of internal body structures in magnetic resonance imaging (MRI)—could improve visualization of liver cancer tumors. MRI is a medical imaging technique commonly used for cancer diagnosis and to track the progress of patients after treatment. Currently, there are two modes of MRI imaging, T1-weighted and T2-weighted imaging, and patients are often given contrast agents to improve imaging quality. Each imaging mode, however, requires a specific class of contrast agent which cannot be used together. This poses a greater challenge in the diagnosis of liver cancer, where T2-weighted imaging is still not considered reliable, and tumor vascularity can confound both T1- and T2-weighted imaging. A research team led by Edward Chow, from the Cancer Science Institute of Singapore at National University of Singapore and the department of pharmacology at NUS Yong Loo Lin of Medicine, has developed a dual-mode contrast agent that enables clearer and more accurate images of tumors from both T1- and T2-weighted MRI scans, and with lower dosages of contrast agent. The dual-mode contrast agent, which the researchers developed with nanodiamonds in combination with a manganese base, provides greater imaging contrast than existing options. The team also found that liver tumors that can’t be visualized without contrast agents become readily visible even at low dosages of the new compound. Contrast agents work by altering the magnetic properties of nearby water molecules, which enhances the quality of MR images. Nanodiamonds, which are carbon-based particles of two to eight nanometers in diameter, have unique chemical properties that allow them to attract water molecules. This enables them to promote proton exchange between water molecules and paramagnetic ions (i.e. contrast agents) that accumulate in tissues. “Our experiments suggest that our dual-mode contrast agent holds great promise in improving imaging for liver cancer,” says Chow. “We are hopeful that this advancement in nanomedicine will lead to safer and more accurate diagnosis of liver cancer. Moving forward, we plan to conduct further preclinical safety studies for our contrast agents, with the end goal being clinical implementation. We are also looking into using our contrast agents to improve imaging for glioma and ovarian cancer,” Chow adds. The study was a collaboration with the NUS Comparative Medicine Imaging Facility and the Agency for Science, Technology, and Research’s Singapore Bioimaging Consortium. The findings of the study appear in the journal Nanomedicine: Nanotechnology, Biology, and Medicine.
News Article | May 4, 2017
Scientists from the National University of Singapore (NUS) have developed a novel nanodiamond-based contrast agent — a chemical "dye" used to enhance the visibility of internal body structures in magnetic resonance imaging (MRI) — that improves visualization of liver cancer tumors. Better and more sensitive imaging contributes towards detecting liver cancer and is crucial for planning for treatment. MRI is a medical imaging technique commonly used for cancer diagnosis and to track the progress of patients after treatment. Currently, there are two modes of MRI imaging, T1-weighted and T2-weighted imaging, and patients are often given contrast agents to improve imaging quality. However, each imaging mode requires a specific class of contrast agent which cannot be used together. This poses a greater challenge in the diagnosis of liver cancer, where T2-weighted imaging is still not considered reliable, and both T1- and T2-weighted imaging can be confounded by tumor vascularity. A research team led by Assistant Professor Edward Chow, Principal Investigator from the Cancer Science Institute of Singapore at NUS and Department of Pharmacology at NUS Yong Loo Lin of Medicine, has developed a dual-mode contrast agent which enables clearer and more accurate images of tumors to be obtained in both T1- and T2-weighted MRI scans, and with lower dosages of contrast agent. The novel dual-mode contrast agent, which was developed using nanodiamonds in combination with a manganese base, provides greater imaging contrast than existing clinical agents which are used to improve quality of MRIs. The team also found that liver tumors that are unable to be visualized without contrast agents become readily visible even at low dosages of the novel compound. Contrast agents work by altering the magnetic properties of nearby water molecules, which enhances the quality of MR images. Nanodiamonds, which are carbon-based particles of two to eight nanometers in diameter, have unique chemical properties that allow them to attract water molecules. This enables them to promote proton exchange between water molecules and paramagnetic ions (i.e. contrast agents) that accumulate in tissues. As a result, T1 and T2 relaxation is enhanced, giving better quality images. This is unlike existing nanotechnology-based approaches, where nanomaterials are used to improve delivery of paramagnetic ions to specific tumor sites. “Our experiments suggest that our dual-mode contrast agent holds great promise in improving imaging for liver cancer. We are hopeful that this advancement in nanomedicine will lead to safer and more accurate diagnosis of liver cancer. Moving forward, we plan to conduct further pre-clinical safety studies for our contrast agents, with the end goal being clinical implementation. We are also looking into using our contrast agents to improve imaging for glioma and ovarian cancer,” says Chow. The study was conducted in collaboration with the NUS Comparative Medicine Imaging Facility and the Agency for Science, Technology and Research’s Singapore Bioimaging Consortium. The findings of the study were published in the scientific journal Nanomedicine: Nanotechnology, Biology and Medicine in April 2017.
News Article | March 4, 2016
Mice were housed in the Unit for Laboratory Animal Medicine at the Whitehead Institute for Biomedical Research and Koch Institute for Integrative Cancer Research. The following strains were obtained from the Jackson Laboratory: Lgr5-EGFP-IRES-CreERT2 (strain name: B6.129P2-Lgr5tm1(cre/ERT2)Cle/J, stock number 008875), Rosa26-lacZ (strain name: B6.129S4-Gt(ROSA)26Sortm1Sor/J, stock number 003474), db/db (strain name: B6.BKS(D)-Leprjb/J, stock number 000697), PpardL/L (strain name: B6.129S4-Ppardtm1Rev/J, stock number 005897). Apcloxp exon 14 (ApcL/L) has been previously described41. Villin-CreERT2 was a gift from S. Robine. Long-term HFD was achieved by feeding male and female mice a dietary chow consisting of 60% kcal fat (Research Diets D12492) beginning at the age of 8–12 weeks and extending for a period of 9–14 months. Control mice were sex- and age-matched and fed standard chow ad libitum. GW501516 (Enzo) was reconstituted in DMSO at 4.5 mg ml−1 and diluted 1:10 in a solution of 5% PEG400 (Hampton Research), 5% Tween80 (Sigma), 90% H O for a daily intraperitoneal injection of 4 mg kg−1. Apc exon 14 was excised by tamoxifen suspended in sunflower seed oil (Spectrum S1929) at a concentration of 10 mg ml−1 and 250 μl per 25 g of body weight, and administered by intraperitoneal injection twice over 4 days before collecting tissue. PpardL/L mice were administered 4–5 intraperitoneal injections of tamoxifen on alternate days. Mice were analysed within 2 weeks of the last tamoxifen injection. BrdU was prepared at 10 mg ml−1 in PBS, passed through a 0.22-μm filter and injected at 100 mg kg−1. As previously described1, tissues were fixed in 10% formalin, paraffin embedded and sectioned. Antigen retrieval was performed with Borg Decloaker RTU solution (Biocare Medical) in a pressurized Decloaking Chamber (Biocare Medical) for 3 min. Antibodies used: rat anti-BrdU (1:2,000 (immunohistochemistry (IHC)), 1:1,000 (immunofluorescence (IF)) Abcam 6326), rabbit chromogranin A (1:4,000 (IHC), 1:250 (IF), Abcam 15160), rabbit monoclonal non-phospho β-catenin (1:800 (IHC), 1:400 (IF), CST 8814S), mouse monoclonal β-catenin (1:200, BD Biosciences 610154), rabbit polyclonal lysozyme (1:250, Thermo RB-372-A1), rabbit polyclonal MUC2 (1:100, Santa Cruz Biotechnology 15334), rabbit monoclonal OLFM4 (1:10,000, gift from CST, clone PP7), Biotin-conjugated secondary donkey anti-rabbit or anti-rat antibodies were used from Jackson ImmunoResearch. The Vectastain Elite ABC immunoperoxidase detection kit (Vector Labs PK-6101) followed by Dako Liquid DAB+ Substrate (Dako) was used for visualization. For immunofluorescence, Alexa Fluor 568 secondary antibody (Invitrogen) was used with Prolong Gold (Life Technologies) mounting media. All antibody incubations involving tissue or sorted cells were performed with Common Antibody Diluent (Biogenex). Organoids were fixed with 4% paraformaldehyde, permabilized with 0.5% Triton X-100 in PBS, rinsed with 100 mM glycine in PBS, blocked with 10% donkey serum in PBS, incubated overnight with primary antibody at 4 °C, rinsed and incubated with Alexa Fluor 568 secondary antibody (Invitrogen), and mounted with Prolong Gold (Life Technologies) mounting media. The in situ hybridization probes used in this study correspond to expressed sequence tags or fully sequenced cDNAs obtained from Open Biosystems. The accession numbers (IMAGE mouse cDNA clone in parenthesis) for these probes are as follows: mouse Olfm4 BC141127 (9055739), mouse Crp4 BC134360 (40134597). Both sense and antisense probes were generated to ensure specificity by in vitro transcription using DIG RNA labelling mix (Roche) according to the manufacturer’s instructions and to previously published detailed methods23, 42. Single-molecule in situ hybridization was performed using Advanced Cell Diagnostics RNAscope 2.0 HD Detection Kit. Adult mice were exposed to 15 Gy of ionizing irradiation from a 137-caesium source (GammaCell) and euthanized after 72 h. The number of surviving crypts per length of the intestine was enumerated from haematoxylin-and-eosin-stained sections15. Antibodies: rabbit polyclonal anti-PPAR-δ (1:100, Thermo PA1-823A), rabbit polyclonal anti-CPT1a (1:250, ProteinTech 15184-1-AP), rabbit polyclonal anti-HMGCS2 (1:500, Sigma AV41562), rabbit monoclonal anti-FABP1 (1:1,000, Abcam ab129203), NF-κB Sampler Pathway Kit (CST, 9936S), mouse monoclonal anti-STAT-3 (CST, 9139P), rabbit monoclonal anti-P-STAT3 (Y705) XP (CST, 9145P), mouse monoclonal anti-CREB (CST, 86B10), mouse monoclonal anti-β-catenin (1:200, BD Biosciences 610154), rabbit polyclonal anti-γ-tubulin (1:1,000, Sigma T5192). For immunoprecipitation assays, crypts were collected and nuclear extraction was carried out using Abcam nuclear extraction kit (ab113474) following manufacturer’s instructions. Nuclear extracts were incubated with 5 μg anti-PPAR-δ antibody (Thermo), or anti-rabbit IgG control antibody (Santa Cruz) overnight at 4 °C followed by 2 h of incubation with Dynabeads Protein G for immunoprecipitation. Protein complexes bound to antibody and beads were washed five times and eluted with Laemmli sample buffer. Samples were resolved by SDS–PAGE. Protein interaction was analysed by immunoblotting. Lgr5-GFPhi ISCs or Lgr5-GFPlow progenitors were sorted directly into Laemmli sample buffer and boiled for 5 min. Samples were resolved by SDS–PAGE and analysed by immunoblotting with horseradish peroxidase (HRP)-conjugated IgG secondary antibodies (1:10,000, Santa Cruz Biotechnology sc-2054) and Western Lightning Plus-ECL detection kit (Perkin Elmer NEL104001EA) As previously reported and briefly summarized here, small intestines and colons were removed, washed with cold PBS without magnesium chloride and calcium (PBS−/−) opened longitudinally, and then cut into 3–5-mm fragments. Pieces were washed several times with cold PBS−/− until clean, washed 2–3 with PBS−/− EDTA (10 mM), incubated on ice for 90–120 min, and gently shook at 30-min intervals. Crypts were then mechanically separated from the connective tissue by more rigorous shaking, and then filtered through a 70-μm mesh into a 50-ml conical tube to remove villus material (for small intestine) and tissue fragments. Crypts were removed from this step for crypt culture experiments and embedded in Matrigel with crypt culture media. For ISC isolation, the crypt suspensions were dissociated to individual cells with TrypLE Express (Invitrogen). Cell labelling consisted of an antibody cocktail comprising CD45-PE (eBioscience, 30-F11), CD31-PE (Biolegend, Mec13.3), Ter119-PE (Biolegend, Ter119), CD24-Pacific Blue (Biolegend, M1/69), CD117-APC/Cy7 (Biolegend, 2BS), and EPCAM-APC (eBioscience, G8.8). ISCs were isolated as Lgr5-EGFPhiEpcam+CD24low/−CD31−Ter119−CD45−7-AAD−. EGFPlow progenitors were isolated as EGFPlowEpcam+CD24low/−CD31−Ter119−CD45−7-AAD−, and Paneth cells from small intestine were isolated as CD24hiSidescatterhiLgr5-EGFP−Epcam+CD31−Ter119−CD45−7-AAD− with a BD FACS Aria II SORP cell sorter into supplemented crypt culture medium for culture. Dead cells were excluded from the analysis with the viability dye 7-AAD (Life Technologies). When indicated, populations were cytospun (Thermo Cytospin 4) at 800 r.p.m. for 2 min, or allowed to settle at 37 °C in fully humidified chambers containing 5% CO onto poly-l-lysine-coated slides (Polysciences). The cells were subsequently fixed in 4% paraformaldehyde (pH 7.4, Electron Microscopy Sciences) before staining. Isolated crypts were counted and embedded in Matrigel (Corning 356231 growth factor reduced) at 5–10 crypts per μl and cultured in a modified form of medium as described previously13. Unless otherwise noted, Advanced DMEM (Gibco) was supplemented by EGF 40 ng ml−1 (R&D), Noggin 200 ng ml−1 (Peprotech), R-spondin 500 ng ml−1 (R&D or Sino Biological), N-acetyl-l-cysteine 1 μM (Sigma-Aldrich), N2 1X (Life Technologies), B27 1X (Life Technologies), Chiron 10 μM (Stemgent), Y-27632 dihydrochloride monohydrate 20 ng ml−1 (Sigma-Aldrich). Colonic crypts were cultured in 50% conditioned medium derived from L-WRN cells supplemented with Y-27632 dihydrochloride monohydrate 20 ng ml−1, as described43. Approximately 25–30 μl droplets of Matrigel with crypts were plated onto a flat bottom 48-well plate (Corning 3548) and allowed to solidify for 20–30 min in a 37 °C incubator. Three hundred microlitres of crypt culture medium was then overlaid onto the Matrigel, changed every 3 days, and maintained at 37 °C in fully humidified chambers containing 5% CO . Clonogenicity (colony-forming efficiency) was calculated by plating 50–300 crypts and assessing organoid formation 3–7 days or as specified after initiation of cultures. Palmitic acid (Cayman Chemical Company 10006627 conjugated to BSA), oleic acid (Sigma O1008), lipid mixture (Sigma L0288), or GW501516 (Enzo) were added immediately to cultures at 30 μM (palmitic acid, oleic acid), 2% (lipid mixture), and 1 μM (GW501516). 4-OH tamoxifen (Calbiochem, 579002, 10 nM) was added to organoid cultures derived from PpardL/L; Villin-CreERT2 (Ppard IKO) crypts to ensure Ppard excision in the ex vivo fatty acid or GW501516 experiments. Isolated ISCs or progenitor cells were centrifuged for 5 min at 250g, re-suspended in the appropriate volume of crypt culture medium (500–1,000 cells μl−1), then seeded onto 25–30 μl Matrigel (Corning 356231 growth factor reduced) containing 1 μM Jagged (Ana-Spec) in a flat bottom 48-well plate (Corning 3548). Alternatively, ISCs and Paneth cells were mixed after sorting in a 1:1 ratio, centrifuged, and then seeded onto Matrigel. The Matrigel and cells were allowed to solidify before adding 300 μl of crypt culture medium. The crypt media was changed every second or third day. Organoids were quantified on days 3, 7 and 10 of culture, unless otherwise specified. For secondary organoid assays, either individual primary organoids or many primary organoids were mechanically dissociated and then replated, or organoids were dissociated for 10 min in TrypLE Express at 32 °C, resuspended with SMEM (Life Technologies), centrifuged (5 min at 250g) and then resuspended in cold SMEM with the viability dye 7-AAD. Live cells were sorted and seeded onto Matrigel as previously described in standard crypt media (not supplemented with lipids or GW501516). Secondary organoids were enumerated on day 4, unless otherwise specified. Human biopsies were obtained from patients with informed consent undergoing intestinal resection at the Massachusetts General Hospital (MGH). The MGH Institutional Review Board committee and Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects approved the study protocols. Crypts were isolated43, embedded in Matrigel and subsequently exposed to lipid mixture, palmitic acid or GW501516 (as described in earlier). Cultures were passaged weekly and maintained for 3–4 weeks. To passage, equal numbers of organoids from each condition were disrupted with trypsin/EDTA. Numbers of organoids were counted 4–7 days after passaging into control media. Counts were normalized to numbers of organoids present in control wells and plotted. Statistical significance was calculated by performing analysis of variance (ANOVA) multiple comparisons of the means for each group. For quantitative RNA expression analysis, organoids were dissociated, cells were selected as a live population by flow cytometry (7-AAD, Life Technologies), and sorted into Tri Reagent (Life Technologies) for RNA isolation. After 5 days of culturing, intestinal organoids were placed into Karnovsky’s KII solution (2.5% glutaraldehyde, 2.0% paraformaldehyde, 0.025% calcium chloride, in a 0.1 M sodium cacodylate buffer, pH 7.4) and fixed overnight. Subsequently, they were post-fixed in 2.0% osmium tetroxide, stained en bloc with uranyl acetate, dehydrated in graded ethanol solutions, infiltrated with propylene oxide/Epon mixtures, flat embedded in pure Epon, and polymerized overnight at 60 °C. Then 1-μm sections were cut, stained with toluidine blue, and examined by light microscopy. Representative areas were chosen for electron microscopic study and the Epon blocks were trimmed accordingly. Thin sections were cut with an LKB 8801 ultramicrotome and diamond knife, stained with Sato’s lead, and examined in a FEI Morgagni transmission electron microscope. Images were captured with an AMT (Advanced Microscopy Techiques) 2K digital CCD camera. For RNA sequencing (RNA-seq), total RNA was extracted from 200,000 sorted Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors by pooling 2–5 71-week-old HFD male or control mice using Tri Reagent (Life Technologies) according to the manufacturer’s instructions, except for an overnight isopropanol precipitation at −20 °C. From the total RNA, poly(A)+ RNA was selected using Oligo(dT) -Dynabeads (Life technologies) according to the manufacturer’s protocol. Strand-specific RNA-seq libraries were prepared using the dUTP-based, Illumina-compatible NEXTflex Directional RNA-Seq Kit (Bioo Scientific) according to the manufacturer’s directions. All libraries were sequenced with an Illumina HiSeq 2000 sequencing machine. For RNA-seq data analysis, raw stranded reads (40 nucleotides) were trimmed to remove adaptor and bases with quality scores below 20, and reads shorter than 35 nucleotides were excluded. High-quality reads were mapped to the mouse genome (mm10) with TopHat version 1.4.1 (ref. 44), using known splice junctions from Ensembl Release 70 and allowing at most two mismatches. Genes were quantified with htseq-count (with the ‘intersect strict’ mode) using Ensembl Release 70 gene models. Gene counts were normalized across all samples using estimateSizeFactors from the DESeq R/Bioconductor package45. Differential expression analysis was also performed between two samples of interest with DESeq. GSEA (http://software.broadinstitute.org/gsea/index.jsp) was performed by using the pre-ranked (according to their ratios) 8,240 differentially expressed genes as the expression data set. Motif Analysis was performed using Haystack motif enrichment tool: http://github.com/lucapinello/Haystack46. In total, 24 single Lgr5-GFPhi ISCs and 72 single Lgr5-GFPlow progenitor cells were sorted from control or HFD-fed mice (n = 2 mice per group) for single-cell gene expression analysis. For one-tube single-cell sequence-specific preamplification, individual primer sets of β-catenin target genes (total of 96, Supplementary Table 2) were pooled to a final concentration of 0.1 mM for each primer. Single cells were directly sorted into 96-well plates containing 5 μl RT–PCR master mix (2.5 μl CellsDirect reaction mix, Invitrogen; 0.5 μl primer pool; 0.1 μl reverse transcriptase/Taq enzyme, Invitrogen; 1.9 μl nuclease-free water) in each well. Immediately after, plates were placed on PCR machine for preamplification. Sequence-specific preamplification PCR protocol was as following: 60 min at 50 °C for cell lysis and sequence-specific reverse transcription; then 3 min at 95 °C for reverse transcriptase inactivation and Taq polymerase activation. cDNA was then amplified by 20 cycles of 15 s at 95 °C for initial denaturation, 15 min at 60 °C for annealing and elongation. After preamplificiation, samples were diluted 1:5 before high-throughput microfluidic real-time PCR analysis using Fluidigm platform. Amplified single-cell cDNA samples were assayed for gene expression using individual qRT–PCR primers and 96.96 dynamic arrays on a BioMark System by following manufacturers protocol (Fluidigm). To confirm PPAR-δ-mediated induction of the most upregulated genes (n = 3 mice, 24 ISCs and 72 progenitors per group), or for single-cell analysis of organoid composition (n = 3 mice, 48 cells per group) and db/db mice (n = 3, 48 cells per group) standard single-cell qRT–PCR was performed using preamplified cDNA with corresponding primers. For Fluidigm analysis, threshold cycle (C ) values were calculated using the BioMark Real-Time PCR Analysis software (Fluidigm). See Supplementary Information for raw gene expression data. Gene expression levels were estimated by subtracting the C values from the background level of 35, which approximately represent the log gene expression levels. The t-Distributed stochastic neighbour embedding (t-SNE) analysis47 was performed using the MATLAB toolbox for dimensionality reduction. Differential expression analysis was conducted using the two-sided Wilcoxon–Mann–Whitney rank sum test implemented in the R coin package (https://www.r-project.org). P values were adjusted for multiple testing48 using the p.adjust function in R with method = ‘fdr’ option. Fold changes were calculated as the difference of median of log expression levels for the two cell populations. Split violin plots were generated using the vioplot package and the vioplot2 function in R (https://gist.github.com/mbjoseph/5852613). The heatmap for β-catenin target genes was generated with the MultiExperiment Viewer (MeV) program (http://www.tm4.org/mev.html) using the correlation-based distance and average linkage method as parameters of the unsupervised hierarchical clustering of genes. The heatmap for organoid composition was generated using MATLAB. The percentages of Jag1/Jag2-upregulated cells were calculated based on the number of single cells whose log expression was above 15. Approximately 25,000 cells were sorted into Tri Reagent (Life Technologies) and total RNA was isolated according to the manufacturer’s instructions with following modification: the aqueous phase containing total RNA was purified using the RNeasy plus kit (Qiagen). RNA was converted to cDNA with the cDNA synthesis kit (Bio-Rad). qRT–PCR was performed with diluted cDNA (1:5) in three wells for each primer and SYBR green master mix (Bio-Rad) on Bio-Rad iCycler RT–PCR detection system. For organoid experiments, 1,000 live cells were sorted and qRT–PCR optimized for low cell numbers (<1,000) was performed after sequence specific pre-amplification (cDNA diluted 1:200 in three wells for each primer) as described in single-cell gene expression analysis. All qRT–PCR experiments were repeated at least three independent times. Primers used are listed on Supplementary Table 1. ApcL/L; Lgr5-EGFP-IRES-CreERT2 mice were treated with vehicle or GW501516 for 1 month, and then injected with two intraperitoneal doses of tamoxifen. Four days later, Apc-null Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors were sorted by flow cytometry, as described earlier. For primary cell transplantations, 10,000 Apc-null Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors were resuspended into 90% crypt culture media (as described) and 10% Matrigel, then transplanted into the colonic lamina propria of C57BL/6 recipient mice by optical colonoscopy using a custom injection needle (Hamilton Inc., 33-gauge, small Hub RN NDL, 16 inches long, point 4, 45 degree bevel, like part number 7803-05), syringe (Hamilton Inc. part number 7656-01), and transfer needle (Hamilton Inc. part number 7770-02). Optical colonoscopy was performed using a Karl Storz Image 1 HD Camera System, Image 1 HUB CCU, 175 Watt Xenon Light Source, and Richard Wolf 1.9mm/9.5 Fr Integrated Telescope (part number 8626.431). Four injections were performed per mouse. Mice then underwent colonoscopy 8 weeks later to assess tumour formation. Colonoscopy videos and images were saved for offline analysis. Following sacrifice, the distal colons were excised and fixed in 10% formalin, then examined by haematoxylin and eosin section to identify adenomas. Histology images were reviewed by gastrointestinal pathologists who were blinded to the treatment groups (S.S., V.D. and Ö.H.Y.). All experiments reported in Figs 1, 2, 3, 4, 5 were repeated at least three independent times, except for Figs 3a, 4c, d, which were repeated twice. All samples represent biological replicates. For mouse organoid assays, 2–4 wells per group with at least 3 different mice were analysed. For human organoid assays, 4 wells per group with 4 different patient samples were analysed and experiments were repeated 4 times. All centre values shown in graphs refer to the mean. For statistical significance of the differences between the means of two groups, we used two-tailed Student’s t-tests. Statistical significance in Fig. 3k was calculated by performing ANOVA multiple comparisons of the means for each group. No samples or animals were excluded from analysis, and sample size estimates were not used. Animals were randomly assigned to groups. Studies were not conducted blinded, with the exception of all histological analyses and Fig. 5c, h. All experiments involving mice were carried out with approval from the Committee for Animal Care at MIT and under supervision of the Department of Comparative Medicine at MIT.
News Article | December 7, 2016
All animal work was approved by the Committee for Animal Care of the Division of Comparative Medicine at the Massachusetts Institute of Technology. Adult (3-month-old) male double transgenic 5XFAD Cre mice were produced by crossing 5XFAD transgenic mice with the transgenic PV or CW2 promoter driven Cre line. Adult (5-month-old) male and female APP/PS1 mice were gifted from the Tonegawa laboratory. Adult (4-month-old) male TauP301S mice were obtained from the Jackson Laboratory. Nine-month-old WT mice (C57Bl/6) were obtained from the Jackson Laboratory. Mice were housed in groups of three to five on a standard 12 h light/12 h dark cycle, and all experiments were performed during the light cycle. Food and water were provided ad libitum unless otherwise noted. Littermates were randomly assigned to each condition by the experimenter. The experimenter was blind to animal genotypes during tissue processing and electrophysiological recording and analysis. No animals were excluded from analysis. Adeno-associated viral (AAV) particles of serotype 5 were obtained from the Vector Core Facility at The University of North Carolina at Chapel Hill. The AAV5 virus contained a channelrhodopsin-2 (ChR2) fused to eYFP in a double-floxed, inverted, open-reading-frame (DIO) driven by the EF1α promoter (Extended Data Fig. 2a). An AAV-DIO–eYFP construct was used as a control. Three-month-old 5XFAD/PV-Cre or CW2 mice were anaesthetized with an intraperitoneal (i.p.) injection of a mixture of ketamine (1.1 mg/kg) and xylazine (0.16 mg/kg). A small craniotomy was made 2.0 mm posterior to bregma and 1.8 mm lateral to the midline on the left side. Virus was delivered through a small durotomy by a glass micropipette attached to a Quintessential Stereotaxic Injector (Stoelting). The glass micropipette was lowered to 1.2 mm below the brain surface. A bolus of 1 μL of virus (AAV-DIO-ChR2–eYFP or AAV-DIO–eYFP; 2 × 1012 viral molecules per millilitre) was injected into the CA1 region of the hippocampus at 0.075 μL min−1. The pipette remained in place for 5 min following the injection before being retracted from the brain. A unilateral optical fibre implant (300 μm core diameter; Thor Labs) was lowered to 0.9 mm below the brain surface about the injection site. Two small screws anchored at the anterior and posterior edges of the surgical site were bound with dental glue to secure the implant in place. For electrophysiological recordings, adult (3-month-old) male 5XFAD/PV-Cre and 5XFAD negative littermates (for CA1 recordings), or 5XFAD and their WT littermates (for VC recordings) mice were anaesthetized using isoflurane and placed in a stereotactic frame. The scalp was shaved, ophthalmic ointment (Puralube Vet Ointment, Dechra) was applied to the eyes, and Betadine and 70% ethanol were used to sterilize the surgical area. For CA1 recordings, a craniotomy (in millimetres, from bregma: −2 anterior/posterior, 1.8 medial/lateral) was opened to deliver 1 μL of virus to CA1 (as described above). The target craniotomy site for LFP recordings was marked on the skull (in mm, from bregma: −3.23 anterior/posterior, 0.98 medial/lateral for CA1 and 2.8 anterior/posterior, 2.5 medial/lateral for VC), three self-tapping screws (F000CE094, Morris Precision Screws and Parts) were attached to the skull, and a custom stainless steel headplate was affixed using dental cement (C&B Metabond, Parkell). On the day of the first recording session, a dental drill was used to open the LFP craniotomies (300–400 μm diameter) by first thinning the skull until ~100 μm thick, and then using a 30-gauge needle to make a small aperture. The craniotomy was then sealed with a sterile silicone elastomer (Kwik-Sil WPI) until recording that day and in between recording sessions. Two to four weeks after virus injection and implant placement (which provided time for the mice to recover and undergo behaviour training for animals used for electrophysiology, and the virus to express in the neurons), CA1 neurons were optogenetically manipulated. A 200 mW, 4,793 nm DPSS laser was connected to a patch cord with a fibre channel/physical contact connector at each end. During the experiment, 1 mW (measured from the end of the fibre) of optical stimulation was delivered for 1 h. For molecular and biochemical analyses, each animal received one of three stimulation protocols: 8 Hz, 40 Hz, or random stimulation (light pulses were delivered with a random interval determined by a Poisson process with an average frequency of 40 Hz). eYFP control animals received 40 Hz stimulation. For electrophysiological recordings, each animal received all stimulation conditions interleaved during recordings. Fifteen minutes before the experiment, 5XFAD mice were treated with saline (control) or picrotoxin (0.18 mg/kg)25. For molecular and biochemical analyses, mice were then placed in a dark chamber illuminated by a light-emitting diode (LED) bulb and exposed to one of five stimulation conditions: dark, light, 20 Hz, 40 Hz (12.5 ms light on, 12.5 ms light off, 60 W), 80 Hz flicker for 1h. For electrophysiological recordings, each animal received dark, light, 40 Hz flicker, or random (light pulses were delivered with a random interval determined by a Poisson process with an average interval of 40 Hz) stimulation conditions interleaved in 10 s blocks during recordings. For CA1 recordings, head-fixed animals ran on an 8-inch spherical treadmill supported by an air cushion through a virtual reality environment, as described in ref. 30. The motion of the spherical treadmill was measured by an optical mouse and fed into virtual reality software31, running in MATLAB (version 2013b, Mathworks). The virtual environment consisted of a linear track with two small enclosures at the ends where the animal could turn (Extended Data Fig. 1a). Animals were rewarded with sweetened condensed milk (diluted 1:2 in water) at each end of the track for alternating visits to each end of the track. Animals learned to run on the virtual linear track over approximately 1 week. The animals were left to recover from the surgery for 1 week, and habituated to handling for 1–2 days before behavioural training began. To learn to manoeuvre on the treadmill and get comfortable in the testing environment, on the first 2 days of training the animals were placed on the spherical treadmill with the virtual reality system off and were rewarded with undiluted sweetened condensed milk. On the second day of training on the spherical treadmill, the animals’ food was restricted to motivate them to run. Animals were restricted to no more than 85% of their baseline weight and typically weighed over 88% of their baseline weight. From the third day until the end of training (typically 5–7 days) the animals were placed on the treadmill for increasing amounts of time (30 min to 2 h) running in the VR linear track. Animals were rewarded with diluted (1:2) sweetened condensed milk at the end of the linear track after traversing the length of the track. Between recording sessions, animals were given refresher training sessions to maintain behavioural performance. For VC recordings, animals ran on the spherical treadmill while exposed to dark, light, or light-flickering conditions (described below in data acquisition). Before recordings, animals learned to manoeuvre on the treadmill and get comfortable in the testing environment by being placed on the spherical treadmill (with the virtual reality system off) and receiving a reward of undiluted sweetened condensed milk. For optogenetic stimulation of CA1 during recording, a 300 μm core optical fibre was advanced through the craniotomy used to deliver virus to CA1 to a depth of 900 μm into the brain. Light pulses that were 1 ms and 1 mW (measured from the end of the fibre) were delivered via a 473 nm DPSS (diode pumped solid state) laser (as described above). To avoid photoelectric artefacts, neural activity was recorded with glass electrodes. LFP electrodes were pulled from borosilicate glass pipettes (Warner) on a filament-based micropipette puller (Flaming-Brown P97, Sutter Instruments), to a fine tip, which was then manually broken back to a diameter of ~10–20 μm and filled with sterile saline. For CA1 recordings the LFP electrode was advanced through the LFP recording craniotomy at an angle 60 degrees posterior to the coronal plane and 45° inferior to the horizontal plane until clear electrophysiological signatures of the hippocampal stratum pyramidale layer were observed (~600–1000 μV theta waves while the animal was running, clearly distinguishable SWRs during immobility, and multiple spikes greater than 150 μV; Extended Data Fig. 1b). For VC recordings, the LFP electrode was advanced vertically through the LFP recording craniotomy to a depth of 600–900 μm and multiple spikes greater than 150 μV were observed. Data were acquired with a sampling rate of 20 kHz and bandpass filtered 1 Hz to 1 kHz. Animals ran on the spherical treadmill or rested for prolonged periods. For optogenetic simulation sessions, data were recorded for 30 min before any stimulation began. Then stimulation was delivered at gamma (40 Hz), random (as described under Optogenetic stimulation protocol), or theta (8 Hz) frequency for 10 s periods interleaved with 10 s baseline periods (no stimulation). In two animals, stimulation of each type or baseline was delivered for 5 min periods instead of 10 s periods. Each 30 min of stimulation recordings were followed by 5–30 min of recording with no stimulation. For visual light flicker simulation sessions, LED striplights surrounding the animal lights were flickered at gamma (40 Hz), random (described above in Visual stimulation protocol), theta (8 Hz), or 20 Hz frequency for 10 s periods, or were on continuously for 10 s periods, interleaved with 10 s periods with lights off. A few recordings were made above the brain surface during light flicker to ensure that the lights did not create electrical or photoelectric noise during recording. Recording sessions were terminated after approximately 3–5 h. Animals were 3–4 months old at the time of recording. Spikes were detected by thresholding the 300–6,000 Hz bandpassed signal. Threshold was the median of the filtered signal plus five times a robust estimator of the standard deviation of the filtered signal (median/0.675) to avoid contamination of the standard deviation measure by spikes32. Recorded traces were downsampled to 2 kHz and then bandpass filtered between 1 and 300 Hz. Activity across the hippocampal network changes markedly when animals run or sit quietly, and these changes are often referred to as different network states. These network states are clearly distinguishable by the presence or absence of LFP oscillations in different frequency bands12, 13. When animals ran, we observed large theta (4–12 Hz) oscillations in CA1 as others have shown (Extended Data Fig. 1b, left)13, 30, 33, 34. When animals sat quietly, theta oscillations were no longer visible and we recorded SWRs, high-frequency oscillations of 150–250 Hz that last around 50–100 ms and are associated with bursts of population activity, as others have observed (Extended Data Fig. 1b, right)15, 16. SWRs were detected (Fig. 1a–d and Extended Data Fig. 1d–i) when the envelope amplitude of the filtered trace was greater than four standard deviations above the mean for at least 15 ms. The envelope amplitude was calculated by taking the absolute value of the Hilbert transform of the filtered LFP. We also confirmed our results held when using a higher threshold for SWR detection, six standard deviations above the mean, which detects larger SWRs (Extended Data Fig. 1j, k). To detect theta (Extended Data Fig. 1c, d), the LFP was bandpass filtered for theta (4–12 Hz), delta (1–4 Hz), and beta (12–30 Hz) using an FIR equiripple filter. The ratio of theta to delta and beta (‘theta ratio’) was computed as the theta envelope amplitude divided by the sum of the delta and beta envelope amplitudes. Theta periods were classified as such when the theta ratio was greater than one standard deviation above mean for at least 2 s and the ratio reached a peak of at least two standard deviations above mean. Non-theta periods were classified as such when the theta ratio was less than one for at least 2 s. SWRs, theta periods, and non-theta periods were visually inspected to ensure that these criteria accurately detected SWRs, theta periods, and non-theta periods, respectively. Spectral analysis was performing using multitaper methods (Chronux toolbox, time-bandwidth product = 3, number of tapers = 5). For examining power spectra without stimulation (Extended Data Fig. 1c, d), only theta periods were included: theta periods greater than 5 s long were divided into 5 s trials and the average power spectral density was computed for each animal over these trials. For examining power spectra during optogenetic (Fig. 1e and Extended Data Fig. 1l) and visual stimulation (Fig. 4a and Extended Data Fig. 4a), data were divided into 10 s trials of each stimulation condition or baseline periods, and the average power spectral density was computed for each animal over these trials. Spectrograms were computed using multitaper methods (Chronux toolbox). The spectrogram was computed for each SWR including a window of 400 ms before and after the peak of the SWR. Then a z-scored spectrogram was computed in each frequency band using the mean and standard deviation of the spectrogram computed across the entire recording session to create a normalized measure of power in units of standard deviation (Fig. 1a and Extended Data Fig. 1e). Instantaneous frequency of gamma during SWRs was computed by bandpass filtering the LFP for 10–50 Hz, taking the Hilbert transform, then taking the reciprocal of the difference in peaks of the transformed signal (Fig. 1b and Extended Data Fig. 1f). Gamma power before, during, and after SWRs was computed by filtering the LFP for low gamma (20–50 Hz) and taking the amplitude of the envelope of the Hilbert transform to get the mean gamma power in 100 ms bins centred on the SWR peak. This was normalized by the mean and standard deviation of the amplitude of the envelope for the entire recording session to get z-scored gamma power for each bin around each SWRs (Fig. 1c and Extended Data Fig. 1g, j). Phase modulation by gamma during SWRs was computed by bandpass filtering the LFP for gamma (20–50 Hz), taking the Hilbert transform, and determining the phase of the resulting signal for each spike that occurred during SWRs (Extended Data Fig. 1h). To measure differences in phase modulation between 5XFAD and WT animals, we used resampling with replacement: a subset of 100 spikes from each recording was randomly selected to create a phase modulation distribution and this was repeated 500 times for each recording (Fig. 1d and Extended Data Fig. 1k). We then measured the depth of modulation for the spike-gamma phase distribution by computing the difference between the peak and trough divided by the sum of the peak and trough for each distribution (Fig. 1d and Extended Data Fig. 1k). To plot stimulus-evoked multiunit firing histograms, spikes were binned in 2.5 ms bins for 100 ms after the start of each light-on pulse and the fraction of spikes in each bin was computed. Mean and standard error were then computed across all light-on periods. To compute differences in multi-unit firing rate between conditions, firing rates were computed for each 10 s period of stimulation or baseline (total number of spikes divided by duration of period). Differences in firing rate were taken between nearby periods of the relevant type of stimulation (firing rate in gamma stimulation period minus baseline or random periods for optogenetic stimulation, firing rate in gamma stimulation period minus baseline, continuous on, or random periods for light flicker stimulation). Differences from all animals were plotted in histograms (Extended Data Figs 1m and 4c) and the median and quartiles of the multiunit firing rates per 40 Hz stimulation, random stimulation, and no stimulation period for each animal were plotted in box plots (Extended Data Figs 1o and 4d). Mice were perfused with 4% paraformaldehyde under deep anaesthesia, and the brains were post-fixed overnight in 4% paraformaldehyde. Brains were sectioned at 40 μm using a vibratome (Leica). Sections were permeabilized and blocked in PBS containing 0.2% Triton X-100 and 10% normal donkey serum at room temperature for 1 h. Sections were incubated overnight at 4 °C in primary antibody in PBS with 0.2% Triton X-100 and 10% normal donkey serum. Primary antibodies were anti-EEA1 (BD Transduction Laboratories; 641057), anti-β-amyloid (Cell Signaling Technology; D54D2), anti-Iba1 (Wako Chemicals; 019-19741), anti-parvalbumin (Abcam; ab32895), and anti-Rab5 (Enzo Life Sciences; ADI-KAP-GP006-E). To confirm ELISA experiments, the anti-Aβ antibody D54D2 was used because it allowed for co-labelling with EEA1 and the anti-Aβ antibody 12F4 was used because it does not react with APP, allowing us to determine whether our labelling was specific to Aβ. For co-labelling experiments, the anti-Aβ antibody 12F4 (Biolegend; 805501) was used. Primary antibodies were visualized with Alexa-Fluor 488 and Alex-Fluor 647 secondary antibodies (Molecular Probes), and cell nuclei visualized with Hoechst 33342 (Sigma-Aldrich; 94403). Images were acquired using a confocal microscope (LSM 710; Zeiss) with a 40× objective at identical settings for all conditions. Images were quantified using ImageJ 1.42q by an experimenter blind to treatment groups. For each experimental condition, two coronal sections from at least three animals were used for quantification. Scale bars are 50 μm. For CA1 imaging, the analysis was restricted to the pyramidal cell layer, except in the case of Iba1+ cells analysis, where the whole field of view was required to image an adequate number of cells. ImageJ was used to measure the diameter of Iba1+ cell bodies and to trace the processes for length measurement. In addition, the Coloc2 plugin was used to measure co-localization of Iba1 and Aβ. Imarisx64 8.1.2 (Bitplane, Zurich, Switzerland) was used for three-dimensional rendering. For counting the ‘plaque number’, deposits of at least 10 μm were included. Fixed brains were sliced into 100 μm coronal sections on a vibratome (Leica VT100S) in 1× PBS. Sections containing VC were selected, with reference to the Allen Mouse Brain Atlas, and incubated in clearing buffer (pH 8.5–9.0, 200 mM sodium dodecylsulfate, 20 mM lithium hydroxide monohydrate, 4 mM boric acid in double-distilled H O) for 2 h, shaking at 55 °C. Cleared sections were washed 3 × 10mins in 1× PBST (0.1% Triton-X100/1XPBS) and put into blocking solution (2% bovine serum albumin/1× PBST) overnight, shaking at room temperature27. Subsequently, three 1 h washes in 1× PBST were performed, shaking at room temperature. Sections were then incubated at 4 °C for 2 days, shaking, with anti-β-amyloid (Biolegend; 805501) and anti-Iba1 (Wako Chemicals; 019-19741) primary antibodies, diluted to 1:100 in 1× PBST. Another set of 3 × 1 h washes in 1× PBST was conducted before sections were incubated for 9 h, shaking at room temperature, in 1:100 1× PBS-diluted secondary antibody mixture. Fragmented Donkey Anti-Rabbit Alexa Fluor 488 (Abcam; ab175694) and Anti-Mouse 568 (Abcam; ab150101) secondary antibodies were used to visualize the primary antibody labelling. Halfway through this incubation period, Hoechst 33258 (Sigma-Aldrich; 94403) was spiked into each sample at a 1:250 final dilution. Sections were then washed overnight in 1× PBS, shaking at room temperature. Before mounting for imaging, slices were incubated in refractive index matching solution (75 g Histodenz, 20 mL 0.1 M phosphate buffer, 60 mL double-distilled H O) for 1 h, shaking at room temperature. Tissue sections were mounted onto microscopy slides with coverslips (VWR VistaVision, VWR International, LLC, Radnor, Pennsylvania, USA) using Fluromount G Mounting Medium (Electron Microscopy Sciences, Hatfield, Pennsylvania, USA). Images were acquired on a Zeiss LSM 880 microscope with the accompanying Zen Black 2.1 software (Carl Zeiss Microscopy, Jena, Germany). Section overview and cellular-level images used for three-dimensional reconstruction were taken using a Plan-Apochromat 63×/1.4 oil differential interference contrast objective. Imarisx64 8.1.2 (Bitplane, Zurich, Switzerland) was used for three-dimensional rendering and analysis. CA1 whole-cell lysates were prepared using tissue from 3-month-old male 5XFAD/PV-Cre mice. Tissue was homogenized in 1 ml RIPA (50 mM Tris HCl pH 8.0, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS) buffer with a hand homogenizer (Sigma), incubated on ice for 15 min, and rotated at 4 °C for 30 min. Cell debris was isolated and discarded by centrifugation at 14,000 r.p.m. for 10 min. Lysates were quantitated using a nanodrop, and 25 μg protein was loaded on a 10% acrylamide gels. Protein was transferred from acrylamide gels to PVDF membranes (Invitrogen) at 100 V for 120 min. Membranes were blocked using bovine serum albumin (5% w/v) diluted in TBS:Tween. Membranes were incubated in primary antibodies overnight at 4 °C and secondary antibodies at room temperature for 90 min. Primary antibodies were anti-APP (Invitrogen; PAD CT695), anti-APP (Sigma; A8967), and anti-β-actin (Abcam; ab9485). Secondary antibodies were horseradish peroxidase-linked (GE Healthcare). Signal intensities were quantified using ImageJ 1.46a and normalized to values of β-actin. We examined tau protein solubility using sequential protein extraction as described in ref. 28. We then probed the detergent insoluble tau fraction using an antibody against Tau5 (Thermo Fisher Scientific; AHB0042). CA1 or VC was isolated from male mice, lysed with PBS or 5M Guanidine HCl, and subjected to Aβ measurement with the use of mouse (for WT experiments) or human (for all other experiments) Aβ or Aβ ELISA kit (Invitrogen) according to the manufacturer’s instructions. We lysed the tissue in phosphate-buffered saline (PBS) to extract the PBS soluble Aβ fraction. The soluble Aβ fraction probably contained monomeric and oligomeric Aβ. Tissue was further treated with guanidine HCl to extract the insoluble Aβ fraction. Aβ was below detectable levels for both flicker and control groups in WT VC and microglia-specific samples. Total RNA was extracted from CA1 isolates using the RNeasy kit (Qiagen). Purified mRNA was used for RNA-seq library preparation using the BIOO NEXTflex kit (BIOO 5138-08) according to the manufacturer’s instructions. Total mRNA (1 μg) was subject to a sequential workflow of poly-A purification, fragmentation, first strand and second strand synthesis, DNA end-adenylation, and adaptor ligation. The libraries were enriched by 15 cycles of PCR reactions and cleaned with Agencourt AMPure XP magnetic beads (Beckman Coulter). The quality of the libraries was assessed using an Advanced Analytical-fragment Analyzer. The bar-coded libraries were equally mixed for sequencing in a single lane on the Illumina HiSeq 2000 platform at the MIT BioMicro Center. The raw fastq data of 50-bp single-end sequencing reads were aligned to the mouse mm9 reference genome using TopHat2.0. The mapped reads were processed by Cufflinks 2.2 with UCSC mm9 reference gene annotation to estimate transcript abundances, and test for differential expression. An average of 26,518,345 sequencing reads was obtained from three stimulated and three non-stimulated mice. Relative abundance of transcript was measured by fragments per kilobase of exon per million fragments mapped (FPKM). Gene differential expression test between treated and untreated groups was performed using Cuffdiff module with an adjusted P value <0.05 for statistical significance (GEO accession number GSE77471). To understand the cellular and molecular mechanisms from our RNA-seq data, 14 of publicly available RNA-seq datasets35 were processed for cell-type-specific analysis. Additionally, 60 publicly available neuron-, microglia-, and macrophage-specific RNA-seq datasets under different chemical and genetic perturbations36, 37, 38, 39, 40, 41 were downloaded and processed using TopHat/Cufflinks pipeline for gene set enrichment (GSEA) statistical analysis. GSEA was used to determine whether a defined gene set from our RNA-seq data are significantly enriched at either direction of a ranked gene list from a particular perturbation study. Genes detected in the public RNA-seq datasets were ranked by log values of fold change (case versus control), from positive to negative values. A defined gene set (in our case, up- or downregulated genes upon gamma treatment) was considered significantly correlated with a perturbation-induced transcriptomic change (either up- or downregulation) when both nominal P value and false discovery rate q value were less than 0.05. The sign of the calculated normalized enrichment score (NES) indicates whether the gene set is enriched at the top or the bottom of the ranked list. The heatmap for differentially expressed genes was generated using a custom R script, and z-score values across all libraries for each gene were calculated on the basis of the gene FPKM values. The box plots for cell-type specificity analysis were also generated by the R program, on the basis of gene FPKM values. The CA1 subregion was isolated from hippocampus of 3-month-old male 5XFAD/PV-Cre mice. Tissue was rapidly frozen using liquid nitrogen and stored at −80 °C, and RNA extracted using the RNeasy kit according to the manufacturer’s protocol (Qiagen). RNA (3 μg) was treated with DNase I (4 U, Worthington Biochemical Corporation), purified using RNA Clean and Concentrator-5 Kit (Zymo Research) according to the manufacturers’ instructions, and eluted with 14 μL DEPC-treated water. For each sample, 1 μg RNA was reverse transcribed in a 20 μL reaction volume containing random hexamer mix and Superscript III reverse transcriptase (50 U, Invitrogen) at 50 °C for 1 h. First strand cDNAs were diluted 1:10 and 1 μL were used for RT-qPCR amplification in a 20 μL reaction (SsoFast EvaGreen Supermix, Bio-Rad) containing primers (0.2 μM). Relative changes in gene expression were assessed using the 2−ΔΔCt method. The primary VC (V1 region) was rapidly dissected and placed in ice-cold Hanks’ balanced salt solution (HBSS) (Gibco by Life Technologies, catalogue number 14175-095). The tissue was then enzymatically digested using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotec, catalogue number 130-092-628) according to the manufacturer’s protocol, with minor modifications. Specifically, the tissue was enzymatically digested at 37 °C for 15 min instead of 35 min and the resulting cell suspension was passed through a 40 μm cell strainer (Falcon Cell Strainers, Sterile, Corning, product 352340) instead of a MACS SmartStrainer, 70 μm. The resulting cell suspension was then stained using allophycocyanin (APC)-conjugated CD11b mouse clone M1/184.108.40.206 (Miltenyi Biotec, 130-098-088) and phycoerythrin (PE)-conjugated CD45 antibody (BD Pharmingen, 553081) according to the manufacturer’s (Miltenyi Biotec) recommendations. FACS was then used to purify CD11b and CD45 positive microglial cells. The cells were sorted directly into 1× PBS (Extended Data Fig. 6a). Code is publicly available upon request from the corresponding author. For electrophysiological data that were not normally distributed, results are presented as medians and quartiles unless otherwise noted. Two-sided Wilcoxon rank sum tests for equal medians were performed to determine whether distributions were significantly different, and Wilcoxon signed rank tests were performed to determine whether distributions were significantly different from zero as these do not assume data are normally distributed. Variability was similar between the groups that were statistically compared. The Bonferroni method was used to correct for multiple comparisons. No statistical method was used to estimate sample size, but it was consistent with previous publications. Molecular and biochemical results are presented as mean + s.e.m. Percentages stated are group means. All statistical analysis used Prism GraphPad software. Normality was determined using the D’Agostino and Pearson omnibus normality test. Variability was similar between the groups that were statistically compared. Comparison data for normally distributed data consisting of two groups were analysed by two-tailed unpaired t-tests. Comparison of normally distributed data consisting of three or more groups was by one-way ANOVA followed by Tukey’s multiple comparisons test. Comparisons for non-normally distributed data were performed using Mann–Whitney tests. The statistical test, exact P values, and sample size (n) for each experiment are specified in the figure legend. For optogenetic ELISA data, two-sided unpaired Student’s t-tests were performed to compare mice from the same litter that received different conditions. No statistical method was used to estimate sample size, but it is consistent with previous publications. Molecular and biochemical analysis used a minimum of three biological replicates per condition. Data are publicly available upon request from the corresponding author.
News Article | February 15, 2017
Keeping blood sugar levels within a safe range is key to managing both type 1 and type 2 diabetes. In a new finding that could lead to fewer complications for diabetes patients, Yale School of Medicine researchers have found that changes in the size of mitochondria in a small subset of brain cells play a crucial role in safely maintaining blood sugar levels. The study is published in the Feb. 9 issue of the journal Cell Metabolism. "Low blood sugar can be as dangerous as high blood sugar," said senior author Sabrina Diano, professor in the Departments of Obstetrics, Gynecology & Reproductive Sciences, Neuroscience, and Comparative Medicine. "We've found that changes in the size of mitochondria -- small intracellular organelles responsible for energy production -- in certain cells in the brain, could be key to maintaining the blood sugar within a safe range." "This new finding adds to our understanding of how the body keeps blood sugar levels within a safe range when sugar levels drop, like during fasting, or when they spike after a meal," Diano added. Diano and her research team designed the study to help understand how neurons in the brain that regulate appetite affect systemic glucose levels. The team used mouse models in which a specific mitochondrial protein, dynamin-related protein 1 (DRP1), was either missing or present in varying amounts in the subset of brain cells that sense circulating sugar levels. The researchers found that depending on whether the mouse was hungry or not, mitochondria displayed dynamic changes in size and shape, driven by the DRP1 protein. "We found that when DRP1 activity in the neurons was missing, these neurons were more sensitive to changes in glucose levels," said Diano, who is also a member of the Program in Integrative Cell Signaling and Neurobiology of Metabolism and the director of the Reproductive Neuroscience Group at Yale University School of Medicine. "What surprised our research team was that these intracellular changes in this small subset of neurons were specifically important to increase blood sugar levels during a fasting period by activating the so-called counter-regulatory responses to hypoglycemia, in which the brain senses lower glucose levels and sends signals to peripheral organs such as the liver to increase glucose production." Diano said the findings suggest that alterations in this mechanism may be critical for the development of hypoglycemia-associated autonomic failure (HAAF), a complication of several diabetes treatments occurring most often in people with type 1 diabetes who must take insulin for survival. Diano's research team will now focus on assessing how mitochondrial morphological changes relate to mitochondrial function in this subset of neurons in the development of HAAF. Other authors on the study include Anna Santoro, Michela Campolo, Chen Liu, Hiromi Sesaki, Rosaria Meli, Zhong-Wu Liu, and Jung Dae Kim. The study was funded by the National Institutes of Health.
News Article | February 28, 2017
New Haven, Conn.-- More than 8 million individuals in the United States have gout, a disease that can cause intense recurrent episodes of debilitating pain, inflammation, and fever. The cause of gout is the accumulation of urate crystals in joints, which continuously reactivate the immune system, leading to activation of the most common type of immune cell in the blood, neutrophils. These periods of immune reactivation are known as flares, and are driven by a protein complex called the NLRP3 inflammasome. Recent work from the laboratory of Vishwa Deep Dixit, Professor of Comparative Medicine and Immunobiology, has shown that the ketone body β-hydroxybutyrate can specifically inhibit the NLRP3 inflammasome. Ketones are byproducts of fat break down in the liver that can serve as alternative metabolic fuels for the brain and heart during periods of low carbohydrate intake, such as fasting, or ketogenic diet. To test if elevating ketones protected against inflammation during gout, a Postdoctoral Fellow in Dixit's lab, Emily Goldberg, and Associate Research Scientist and Clinical Veterinarian in Comparative Medicine, Jennifer Asher, and their colleagues collaborated to develop a novel model of gout flares in rats. They found that feeding rats a high-fat, low-carbohydrate ketogenic diet increased β-hydroxybutyrate levels and protected rats from joint swelling, tissue damage, and systemic inflammation normally seen during gout. "In isolated neutrophils, β-hydroxybutyrate completely blocked NLRP3 inflammasome activation, even when provided at low concentrations that are physiologically achievable through dietary modification," said Goldberg. She speculated that specifically targeting the NLRP3 inflammasome to reduce inflammation during a flare could improve gout patients' outcomes, but more studies need to be performed to test this possibility.