Flow Cytometry Core Facility
Flow Cytometry Core Facility
News Article | February 15, 2017
No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. Cell lines used in this study were obtained from American Type Culture Collection (ATCC) and cultured under standard conditions. HCT116 cells were authenticated by karyotyping. All cell lines were confirmed to be free of mycoplasma contamination. EGFP was PCR amplified from EGFP-hAGO2 (Addgene catalogue number 21981) and cloned into pMSCV-Puro (Clontech) using the BglII and XhoI restriction sites. The puromycin resistance cassette was then removed by EcoRI and ClaI digestion and replaced with an insert containing eight imperfect miR-19 binding sites (modelled from ref. 39), synthesized as a gBlock (IDT) (sequence in Supplementary Table 8). For the EGFP-only reporter, the puromycin resistance cassette was removed by EcoRI and ClaI digestion followed by re-ligation after filling-in overhangs. Reporters for miR-16 and miR-200c were generated by replacing the puromycin cassette in the pMSCV-Puro vector containing EGFP by digesting with EcoRI and ClaI and ligating in oligonucleotides containing single miRNA binding sites (sequences in Supplementary Table 8). Multiple cloning cycles were performed using MfeI and ClaI to generate the final reporters containing eight total binding sites. MSCV-EGFP, MSCV-EGFP-miR-19, MSCV-EGFP-miR-16, and MSCV-EGFP-miR-200 retrovirus was generated by first seeding 6 × 105 cells per well in a six-well dish. The following day, cells were transfected using 1 μg of plasmid (MSCV-EGFP or MSCV-EGFP-miR-19), 3 μl of FuGENE HD (Promega), and 200 μl Opti-MEM (Thermo Fisher) per well according to the manufacturer’s instructions. Media were changed the next day. Two days after transfection, media were collected and passed through a 0.45 μm SFCA sterile filter. Recipient HCT116 cells were transduced overnight at a multiplicity of infection (MOI) of approximately 0.2 using media supplemented with 8 μg/ml polybrene (EMD Millipore). Cells expressing EGFP were enriched by FACS and single-cell clonal lines were derived. Heterogeneous knockout cell populations were generated using lentiCRISPR v2 (Addgene catalogue number 52961) or lentiCRISPR-hygro. lentiCRISPR-hygro was constructed by replacing the puromycin resistance open reading frame (ORF) in lentiCRISPR v2 with a hygromycin resistance ORF. A silent mutation was introduced into a BsmBI restriction site within the hygromycin resistance ORF to prevent fragmentation of the vector when cloning sgRNA oligonucleotides. sgRNA sequences (Supplementary Table 8) were cloned as described previously12. An sgRNA targeting an irrelevant gene (PPID) or a non-targeting guide were used as negative controls. To generate active lentivirus, 6 × 105 293T cells were first seeded in six-well dishes and transfected the following day using a 5:3:2 ratio of lentiCRISPR:psPAX2 (Addgene catalogue number 12260):pMD2.G (Addgene catalogue number 12259) using FuGENE HD and 1 μg of total plasmid per well. Media were changed the next day. Two days after transfection, media were collected and passed through a 0.45 μm SFCA sterile filter. Media containing the virus were diluted 1:1 with fresh media and used to transduce recipient cells overnight in a final polybrene concentration of 8 μg/ml. Media were changed 24 h later, and cells were split into fresh media containing 1 μg/ml puromycin 48 h after transduction. To generate clonal knockout lines, single-cell cloning was performed after infection with lentiCRISPR v2, lentiCRISPR-hygro, or after transient transfection of PX330 (Addgene catalogue number 42230) targeting the gene of interest. lentiCRISPR v2-derived clones were used in Figs 2d, 4d, e and 5a, e and Extended Data Figs 2, 4e, 5c, and 9a, b. A lentiCRISPR-hygro derived ANKRD52–/– clone was used in Fig. 2e. PX330-derived clones were used in Figs 2a, f and Extended Data Figs 3, 4c, 6, 7, 8c, d, 9a, b, d and 10. Three hundred thousand reporter cells were seeded per well in six-well dishes. Cells were transfected the following day with a mixture of inhibitors for miR-19a and miR-19b at 5 nM each (MiRIDIAN microRNA Hairpin Inhibitors, GE Dharmacon) using Lipofectamine RNAiMAX (Thermo Fisher). Fluorescence was assessed by flow cytometry 48 h after transfection. Lentiviral sgRNA library production. The human GeCKO v2 library was obtained from Addgene (catalogue number 1000000048) and amplified according to the provided instructions. Plasmid was purified from bacterial pellets using a Qiagen Plasmid Maxi Kit. Active lentivirus was prepared in 293T cells by first seeding 3.2 × 106 cells per 10-cm dish. GeCKO library A and library B were prepared independently using 15 dishes per library. The day after seeding, each dish was transfected using 10 μg of total plasmid (5:3:2 ratio of GeCKO library:psPAX2:pMD2.G), 30 μl of FuGENE HD, and 900 μl of Opti-MEM. Medium was exchanged the following day. Media collections at 48 and 72 h after transfection were pooled before filtering through a 0.45 μm SFCA sterile filter. Aliquots of the library were snap frozen on dry ice and ethanol before being stored at −80 °C. Library titre was determined as described12. Transduction of reporter cell lines with lentiCRISPR library. Genome-wide CRISPR–Cas9 screens using HCT116EGFP-miR-19, HCT116EGFP, or ANKRD52–/– HCT116EGFP-miR-19 cells were performed using both GeCKO v2 libraries A and B. Biological replicates were performed for all screens. For each transduction, five 12-well plates were seeded with 5 × 105 reporter cells per well. An overnight transduction was performed the following day by diluting virus to an MOI of 0.2–0.4 in 8 μg/ml polybrene. Cells were then trypsinized and pooled before being plated into fresh medium in six 15-cm dishes. Forty-eight hours later, cells were trypsinized, pooled, counted, and seeded into five 15-cm dishes with 1 μg/ml puromycin using 2.4 × 107 cells per dish. In parallel, a small aliquot of cells was used to confirm that an MOI of 0.2–0.4 was achieved. Cells were passaged for 12–14 days before sorting. At every passage, 1 × 107 cells were seeded per dish into four 15-cm dishes with medium containing puromycin. At least 2 × 107 cells were transduced with each library for each screen, corresponding to ~300× or greater coverage. Cell sorting. Two days before sorting, ten 15-cm dishes with 1.2 × 107 cells per dish were seeded for each library–reporter pair. Samples were prepared for FACS by trypsinization in 0.25% trypsin-EDTA (Thermo Fisher) for 7 min. Cells were dissociated by pipetting up and down approximately 20 times with a P1000 pipet to minimize doublets. Dissociated cells were pipetted directly into media, pelleted at 300g for 5 min, and washed once with PBS. Cells were resuspended at 1.4 × 107 cells per millilitre in PBS supplemented with 3% FBS. Cells were sorted at the University of Texas Southwestern Flow Cytometry Core Facility using a MoFlo cell sorter (Beckman Coulter). The brightest or dimmest 0.5% of cells were collected on the basis of EGFP fluorescence. Cell sorting was performed on approximately 9 × 107 cells, and typical yields ranged from 2 × 105 to 3 × 105 sorted bright/dim cells. Cells were pelleted at 300g and frozen at −80 °C for genomic DNA (gDNA) extraction. Unsorted cells were similarly collected. Genomic DNA extraction. gDNA was extracted from the unsorted cells using a Qiagen DNeasy Blood & Tissue Kit according to the manufacturer’s instructions. Extractions were performed on 4 × 107 cells using 5 × 106 cells per column to ensure enough gDNA for 300× coverage of the library. DNA was eluted by adding 125 μl of water to each column. The same eluate was added back to the column for a second elution. The DNA concentration in the final eluate was assessed using a Qubit dsDNA BR assay kit (Thermo Fisher). To facilitate maximum recovery of gDNA from the sorted cells, a previously described method40 was used with the following modifications: sorted cell pellets were resuspended in 500 μl of tissue lysis buffer, consisting of 460 μl of STE buffer (1 mM EDTA (pH 8.0), 10 mM Tris-HCl (pH 8.0), 100 mM NaCl) supplemented with 10 μl of 0.5 M EDTA, 10 μl of proteinase K (10 mg/ml in TE buffer containing 10 mM Tris-HCl (pH 8.0) and 1 mM EDTA), and 20 μl of 10% SDS. Pellets were digested overnight at 55 °C while shaking at 1,000 r.p.m. on a Thermomixer (Eppendorf). The following day, 5 μl of 2 mg/ml RNase A was added to each tube and incubated at 37 °C for 1 h while shaking at 1,000 r.p.m. Extractions were performed with an equal volume of pH 7.9-buffer saturated phenol, followed by phenol:chloroform:isoamyl alcohol (25:24:1), followed by chloroform. Twenty micrograms of glycogen (Roche) and 1.5 ml of 100% ethanol were added to each tube and DNA was precipitated at −80 °C for 1 h followed by centrifugation at 18,000g for 10 min at 4 °C. Pellets were washed with 1 ml of 75% ethanol, dried, and resuspended in 21 μl of water by incubating at 37 °C for a minimum of 4 h. DNA concentration was determined with the Qubit dsDNA BR assay kit. Sequencing library preparation. Methods to prepare PCR amplicon libraries for deep sequencing were adapted from a previously published protocol12. All primer sequences are provided in Supplementary Table 8. For unsorted cells, an initial round of PCR (PCR I) was performed using 6.6 μg of gDNA per 100 μl PCR reaction. To maintain 300× coverage, 20 reactions were assembled for each sample. For sorted cells, all extracted gDNA for a given sample was distributed into two 100 μl reactions. In both cases, 18 cycles of amplification were performed using Herculase II Fusion polymerase (Agilent). All reactions for a given sample from PCR I were then pooled together and a second round of PCR (PCR II) was performed to add the necessary adapters for Illumina sequencing. Owing to variable PCR efficiency between samples, the cycle number for PCR II was adjusted so that each library was amplified in a 50 μl reaction to a common endpoint with respect to DNA quantity (approximately 50 ng of DNA library in a 50 μl PCR sample). DNA was purified for sequencing using AMPure XP beads (Agencourt) according to the manufacturer’s instructions with the following modifications: each 50 μl PCR II reaction was mixed with 25 μl of beads and incubated for 5 min. Magnetic separation was used to collect the supernatant. The supernatant was mixed with 90 μl of beads and incubated for 5 min. The supernatant was collected and discarded. Beads were washed twice with 200 μl of 70% ethanol and then dried for approximately 12 min. Bound DNA was eluted from the beads using 40 μl of water. Next-generation sequencing. Before sequencing, all DNA libraries were analysed using a Bioanalyzer High Sensitivity DNA Analysis Kit (Agilent). Library concentration was then determined by qPCR using a KAPA Library Quantification Kit for Illumina platforms. All samples were sequenced on an Illumina HiSeq 2500 or a NextSeq 500 with 75 bp single reads. Approximately 15 million to 20 million reads were sequenced per library. Sequencing data analysis. A reference file for all sgRNAs in the library was acquired from Addgene, and identical sgRNAs targeting more than one protein-coding gene were removed. Demultiplexed FASTQ files were mapped to the reference file using Bowtie 2 requiring unique alignments with no mismatches. Normalized read counts were calculated as described previously12. Screen hits were identified using RIGER16 with the following parameters: log(fold-change ranking), 1 × 106 permutations, second-best rank (SBR) scoring algorithm. RNA was extracted from cells using a miRNeasy Mini Kit (Qiagen) with an on-column DNase digestion. cDNA was generated using either the SuperScript IV First-Strand Synthesis System (Thermo Fisher) or MultiScribe Reverse Transcriptase (Thermo Fisher). SYBR Green assays were performed using SYBR Green PCR Master Mix (Applied Biosystems) with custom primer pairs or qRT–PCR assays for mature miRNAs or mRNAs were performed using pre-designed assays and the TaqMan Universal Master Mix II (Applied Biosystems). Primer sequences and catalogue numbers provided in Supplementary Table 8. A custom Taqman assay was designed for pri-miR-17-92 (sequences provided in Supplementary Table 8). For all co-immunoprecipitation assays, 3.2 × 106 293T cells were seeded 1 day before transfection. Cells were transfected using FuGENE HD with 10 μg of total plasmid. Media were changed the following day. Cells were harvested 48 h after transfection. Cells were washed once, scraped in PBS, and lysed on ice for 10 min in 1 ml of lysis buffer composed of 25 mM Tris-HCl (pH 8.0), 150 mM NaCl, 2 mM MgCl , 0.5% NP-40, 1 mM DTT, and a protease inhibitor cocktail (cOmplete EDTA-free, Roche). Lysates were spun at 10,000g for 10 min. Supernatants were collected and diluted with 0.5 volumes of fresh lysis buffer. One and a half microlitres of immunoprecipitation antibody (anti-V5 (Invitrogen catalogue number 46-0705) or anti-HA (Cell Signaling catalogue number 2367S)) were added to each sample and rotated at 4 °C for 30 min. Thirty microlitres of washed Dynabeads Protein G (Thermo Fisher) were added to each sample and incubated for 6 h. RNase A (Thermo Fisher) was added to a final concentration of 20 μg/ml where indicated. Samples were washed four times in ice-cold lysis buffer. Fifty microlitres of 2× Laemmli sample buffer were added to each sample and aliquots were used for western blot analysis. Antibodies used for western blotting included anti-HA (2367S, Cell Signaling), anti-V5 (46-0705, Invitrogen), anti-AGO2 (SAB4200085, Sigma), anti-GAPDH (2118S, Cell Signaling), anti-α-tubulin (T6199-200UL, Sigma), anti-BRD4 (13440S, Cell Signaling), anti-CTNNB1 (9587S, Cell Signaling), anti-POU2F1 (8157S, Cell Signaling), anti-ANKRD52 (A302-372A, Bethyl), and anti-CSNK1A1 (sc-6477, Santa Cruz). SDS–PAGE gels (7%) were supplemented with Phos-tag AAL solution (Wako) according to the manufacturer’s recommendations. Gels were run at 100 V in an XCELL SureLOCK Mini-Cell (Invitrogen) until the dye front completely exited the gel. Gels were incubated in transfer buffer supplemented with 1 mM EDTA for 10 min. Gels were then soaked in normal transfer buffer for 10 min. Proteins were transferred to a nitrocellulose membrane and standard western blotting procedures were subsequently followed. For lambda phosphatase treatments, lysates were generated as described in the co-immunoprecipitation assays. Lysate (50 μl) was mixed with 10× MnCl buffer and 10× reaction buffer provided with the lambda protein phosphatase kit (NEB). Samples treated with enzyme received 1 μl of purified lambda protein phosphatase. Incubations were performed for 45 min at 30 °C, and samples were subjected to chloroform–methanol precipitation41 before Phos-tag electrophoresis. Endogenous AGO2 was purified from ANKRD52+/+ and ANKRD52–/– HCT116 cells. AGO2–/– cells were used as a control. Ten million cells were seeded per 15-cm dish, and eight dishes were used per cell line. AGO2 was immunoprecipitated using methods adapted from an established protocol42 with 100 μl of Dynabeads Protein G loaded with 18 μg of anti-AGO2 antibody (SAB4200085, Sigma) per purification. Immunoprecipitation eluates were resuspended in 5× Laemmli sample buffer. FH-AGO2 constructs (WT, T830A, S824A/T830A) were stably expressed using MSCV-puro in ANKRD52–/– cells. Ten million cells were seeded per 15-cm dish, and eight dishes were used per cell line. Media were changed 48 h later. Cells were scraped in PBS 72 h after plating. Lysates were generated using methods similar to the co-immunoprecipitation assays, with the exception that a phosphatase inhibitor cocktail (PhosStop, Roche) was included and lysate supernatants were diluted with one volume of lysis buffer. Proteins were immunoprecipitated using 100 μl of Dynabeads Protein G loaded with 20 μg of anti-Flag antibody (F1804, Sigma). Beads were rotated at 4 °C for 3 h. Beads were washed five times in lysis buffer. Proteins were eluted using 70 μl of 2× Laemmli sample buffer per 100 μl of beads. Purified AGO2 proteins were separated by SDS–PAGE and stained using InstantBlue (Expedeon). Gel slices containing AGO2 bands were reduced by DTT, alkylated by iodoacetic acid, and digested with trypsin (Trypsin Gold; Promega). The digestion was stopped by adding formic acid, followed by peptide extraction in acetonitrile. Extracted peptides were desalted by C18 ZipTip (Millipore). Peptide mixtures were separated by C-18 resin (100 Å, 3 μm, MICHROM Bioresources) in-house packed into a silica capillary emitter (100 μm ID, 100 mm resin length). LC gradient was generated by a Dionex Ultimate 3000 nanoLC system (Thermo Scientific), with mobile phase A: 0.1% formic acid and B: 0.1% formic acid in acetonitrile. Mobile phase gradient: 2% B at 0–15 min, 30% B at 81 min, 35% B at 85 min, 40% B at 87 min, 60% B at 95 min, 80% B at 96–107 min and 2% B at 108–120 min. Flow rate: 600 nl/min at 0–13.5 min, 250 nl/min at 13.5–120 min. Peptide eluents were sprayed online with a nano-electrospray ion source (Thermo Scientific) at spray voltage of 1.5 kV and capillary temperature of 250 °C. High-resolution MS analysis was performed on a QExactive Quadrupole-Orbitrap Hybrid mass spectrometer (Thermo Scientific), operating in data-dependent mode with dynamic exclusion of 30 s. Full-scan MS was acquired at an m/z range of 300–1650, resolution of 70,000, and automatic gain control target of 3 × 106 ions. The top 15 most intense ions were subsequently selected for higher-energy collisional dissociation fragmentation at resolution of 17,500, collision energy of 27 eV, and automatic gain control target of 1 × 105 ions. Proteome data analysis used Mascot (Matrix Science) and Proteome Discoverer (1.4, Thermo Scientific). The raw data were searched against the human proteome database (Uniprot, UP000005640) plus common contaminants. Static modification was cysteine carbamidomethylation; variable modifications were serine or threonine phosphorylation, methionine oxidation, and glutamine or asparagine deamination. Precursor mass tolerance was 20 p.p.m. and fragment mass tolerance, 0.05 Da. The maximum number of miscleavage sites allowed was 2. After peptide identification, precursor ion intensities were quantified manually in XCalibur using extracted ion chromatogram. Sequences of all primers used for cloning are provided in Supplementary Table 8. Flag–HA-AGO2 (FH-AGO2) was PCR amplified from pIRES-neo-Flag/HA AGO2 (Addgene catalogue number 10822) and subcloned into pcDNA3.1+. FH-AGO2 mutants were generated using a QuikChange II XL Site-Directed Mutagenesis Kit (Agilent) or by cloning customized gBlocks (IDT) into the parental pcDNA3.1+ vector containing FH-AGO2 (sequence of all mutants provided in Supplementary Table 8). Stable expression of wild-type or mutant FH-AGO2 was achieved in one of two ways. In one, constructs were subcloned into pMSCV-puro (Clontech). In another, stable expression of AGO2 for RNA-seq and eCLIP experiments was achieved by cloning individual mutants into a modified pLJM1-EGFP vector (Addgene catalogue number 19319) where EGFP was resected using AgeI and BsrGI before blunt-end ligation. AGO2 constructs were introduced at the EcoRI cloning site. Flag–HA-AGO1 was subcloned from pIRESneo-Flag/HA AGO1 (Addgene catalogue number 10820) into pMSCV-PIG (Addgene catalogue number 21654). V5-tagged ANKRD52 (corresponding to NP_775866.2) was constructed by PCR amplification from HCT116 cDNA followed by cloning into pcDNA3.1+. cDNA clones for human PPP6C and CSNK1A1 were obtained from the Invitrogen Ultimate ORF LITE Library (Clone ID IOH7224 and IOH59150, respectively) and subcloned into pCAGIG (Addgene catalogue number 11159) using Gateway LR Clonase (Thermo Fisher). For tethering assays, a 5× BoxB sequence adapted from a previous report32 was designed as a gBlock (IDT) and cloned in the XbaI site of pGL3-Control (Promega) (sequence in Supplementary Table 8). For the λN constructs, a gBlock containing the λN peptide sequence with an HA tag32 was subcloned into pcDNA3.1-FH-AGO2, replacing the Flag–HA tag. To generate control plasmid expressing λN-HA peptide alone, the λN-HA sequence was PCR amplified and cloned into pcDNA3.1+. Active lentivirus was generated using FH-AGO2 mutants (WT, 5XA, S828A, and empty vector) cloned into a modified pLJM1 vector with EGFP resected. A viral packaging protocol analogous to that used for the lentiCRISPR lentivirus preparations was used. Recipient ANKRD52–/– HCT116EGFP-miR-19 cells were transduced at an MOI of approximately 0.2. Transduced cells were selected in puromycin for at least 10 days, before use in flow cytometry experiments (Fig. 2f). For experiments involving endogenous AGO2, HCT116EGFP-miR-19 cells were used. For analysis of FH-AGO2 miRNA or mRNA binding, cells stably expressing the indicated wild-type or mutant FH-AGO2 protein were first generated by infecting AGO2–/– HCT116 cells with MSCV retroviruses. Then, for each immunoprecipitation sample, 6 × 106 cells were seeded per 10-cm dish. Cells were harvested 48 h later by scraping in PBS. Pelleted cells were resuspended in 1 ml of a lysis buffer consisting of 25 mM Tris-HCl (pH 8.0), 150 mM NaCl, 2 mM MgCl , 0.5% NP-40, 1 mM DTT, a protease inhibitor cocktail (cOmplete, EDTA-free, Roche), and 250 U/ml Recombinant RNasin Ribonuclease Inhibitor (Promega). Cells were lysed on ice for 10 min. Samples were spun at 10,000g for 10 min. Supernatant fractions were retained. Protein concentration was determined using a Bio-Rad DC Protein Assay Kit, and all samples were adjusted to the same concentration with lysis buffer. Dynabeads Protein G (Thermo Fisher) were prepared by pre-incubating with 1.5 μg of antibody (either anti-Flag (F1804, Sigma) or anti-AGO2 (SAB4200085, Sigma)) and pre-blocking with 0.5 mg/ml BSA, 0.5 mg/ml yeast tRNA, and 0.2 mg/ml heparin. Each sample was incubated with 25 μl of prepared Dynabeads Protein G for 3 h at 4 °C. Samples were washed three times in lysis buffer. Captured protein was eluted from the beads using either 2.5 mg/ml 3× Flag peptide (Sigma) or 3.5 mg/ml AGO2 peptide (sequence derived from ref. 42, synthesized at the University of Texas Southwestern Protein Chemistry Technology Core) dissolved in lysis buffer. Eighty per cent of the eluate was harvested for RNA extraction and 20% was diluted with 2× Laemmli sample buffer for western blot analysis. For each immunoprecipitation, qRT–PCR assays were performed to determine input and immunoprecipitation levels for mature miRNAs and mRNA targets of interest. Western blot analysis determined the relative amount of AGO2 in the immunoprecipitation eluate. RNA quantity as a percentage of input was determined for all immunoprecipitation eluates and then normalized to the relative amount of protein captured in each eluate. Experiments to capture AGO2 loaded with miRNA were adapted from a previously published method30. ANKRD52+/+ and ANKRD52–/– HCT116EGFP-miR-19 cells were seeded at 1.35 × 107 cells per dish in six 15-cm dishes per cell line. Forty-eight hours later, cells from each dish were scraped in PBS, pelleted, and lysed on ice for 10 min in 1 ml of a buffer containing 25 mM Tris-HCl (pH 8.0), 150 mM NaCl, 2 mM MgCl , 0.5% NP-40, 1 mM DTT, a protease inhibitor cocktail (cOmplete, EDTA-free, Roche), a phosphatase inhibitor cocktail (PhosStop, Roche), and 250 U/ml Recombinant RNasin Ribonuclease Inhibitor (Promega). Lysates were spun at 10,000g for 10 min and supernatants were further diluted with one volume of lysis buffer. To assess binding of AGO2 to the target mimic, 1.8 ml of each lysate was incubated with 50 μl of washed Dynabeads MyOne Streptavidin C1 (Thermo Fisher) pre-loaded with 300 pmol of wild-type or mutant RNA oligonucleotide (Supplementary Table 8) and pre-blocked with 1 mg/ml BSA, 0.5 mg/ml yeast tRNA, and 0.2 mg/ml heparin. To assess AGO2 phosphorylation after immunoprecipitation, 1.8 ml of each lysate was incubated with 50 μl of washed Dynabeads Protein G (Thermo Fisher) pre-incubated with 5 μl of anti-AGO2 antibody (SAB4200085, Sigma42) and pre-blocked as noted previously. Lysates were incubated with beads for 3 h at room temperature. Beads were washed four times in lysis buffer before 50 μl of 2× Laemmli sample buffer was added. Phos-tag electrophoresis was performed on captured protein complexes and on input protein samples subjected to chloroform–methanol precipitation41. The 293T cells were seeded in 24-well plates using 7.5 × 104 cells per well. Cells were transfected the following day using FuGENE HD and 301 ng of total plasmid. Each transfection consisted of 1 ng of phRL-SV40 (Promega), 20 ng of pGL3-Control or pGL3-BoxB, 150 ng of pcDNA3.1+ (expressing tethered or untethered proteins), and 130 ng of empty pcDNA3.1+. Cells were harvested 24 h later for luciferase activity assays using a Dual-Luciferase Reporter Assay System (Promega). Firefly luciferase activity was normalized to Renilla luciferase activity in each well to control for variation in transfection efficiency. Biological triplicates were performed for each transfection. ANKRD52–/– HCT116EGFP-miR-19 cells were seeded in six-well dishes at 6 × 105 cells per well. The following day, cells were treated with 10, 50, or 200 nM rapamycin for 72 h (fresh medium with rapamycin was exchanged at 48 h). Cells were harvested in 2× Laemmli sample buffer at the experimental endpoint. AGO2–/– cells were infected with MSCV retroviral constructs to stably express FH-AGO2WT or FH-AGO25XA. FH-AGO2-expressing cells were seeded using 1.5 × 107 cells per dish in 15-cm dishes with three dishes per cell line. Lysates were generated using methods similar to the co-immunoprecipitation assays, with the exception that 2 ml of lysis buffer was used per dish. Lysates were diluted with one volume of lysis buffer. FH-AGO2 was immunoprecipitated using 9 μg of anti-Flag antibody (F1804, Sigma) and 150 μl of washed Dynabeads. Samples were rotated at 4 °C overnight. Beads were washed three times with lysis buffer and then treated with lambda protein phosphatase (NEB) for 45 min. Beads were washed three times with lysis buffer and then resuspended in 100 μl reaction buffer composed of 25 mM Tris-HCl (pH 7.5), 10 mM MgCl , 2.5 mM DTT, 0.01% Triton X-100, 0.5 mg/ml BSA, 0.5 mM EGTA, 0.5 mM Na VO , 5 mM β-glycerophosphate, 170 ng of recombinant CSNK1A1 (PV3850, Thermo Fisher), and 200 μM [γ-32P]ATP (SA = 100–500 c.p.m./pmol). Reactions were incubated at 37 °C for 2 h. Beads were separated and mixed with 50 μl of 2× Laemmli sample buffer. SDS–PAGE was performed, and gels were stained using SimplyBlue SafeStain (Invitrogen). 32P signal was detected using a phosphor screen (GE Healthcare) and Typhoon FLA 7000 (GE Healthcare). In vitro CSNK1A1 kinase assays were performed using assay conditions adapted from the manufacturer’s recommendations (Recombinant CSNK1A1, PV3850, Thermo Fisher). All reactions were performed in a 50 μl volume for 90 min at 30 °C. Assay buffer was composed of 25 mM Tris-HCl (pH 7.5), 10 mM MgCl , 2.5 mM DTT, 0.01% Triton X-100, 0.5 mg/ml BSA, 0.5 mM EGTA, 0.5 mM Na VO , 5 mM β-glycerophosphate, 1 mM peptide (Supplementary Table 8), 170 ng of recombinant CSNK1A1, and 200 μM [γ-32P]ATP (SA = 100–500 c.p.m./pmol). Reactions were terminated using 75 mM H PO and spotted onto P81 phosphocellulose squares. Samples were washed four times in 75 mM H PO for 5 min per wash and immersed in acetone for 5 min before drying. 32P incorporation was assessed by Cerenkov counting. The linear form of ciRS-7 was constructed by amplifying the endogenous ciRS-7 locus from human genomic DNA (Roche) by PCR (Phusion Polymerase, Thermo Scientific) using primer sequences described previously36 (Supplementary Table 8). The PCR fragment was then cloned into the HindIII and NotI cloning sites of pcDNA3.1+ (Invitrogen). To generate the ciRS-7 construct capable of circularization, an ~800-bp region upstream of the splice acceptor was amplified using previously described primers36 (Supplementary Table 8) and inserted in the inverse orientation downstream of the linear ciRS-7 sequence at the XhoI cloning site of pcDNA3.1+. The effect of ciRS-7 expression on AGO2 phosphorylation was assessed through co-transfection experiments. Cells were seeded at a density of 9 × 105 cells per well in six-well dishes. Cells were transfected according to the manufacturer’s recommendations using Lipofectamine 2000 (Thermo Fisher). Where indicated, each well received 2 μg of plasmid and 10 nM miRNA mimics (miRIDIAN miRNA mimics, GE Dharmacon). Cells were harvested 28 h later for western blot analysis. Parental HCT116EGFP-miR-19, AGO2–/– HCT116EGFP-miR-19, ANKRD52–/– HCT116EGFP-miR-19, and ANKRD52–/–;CSNK1A1–/– HCT116EGFP-miR-19 cells were used for RNA-seq. Three independent clonal AGO2–/–, ANKRD52–/–, and ANKRD52–/–;CSNK1A1–/– knockout cell lines and three biological triplicates of parental cells were sequenced. Five hundred thousand cells were seeded per well in six-well dishes. Cells were harvested 48 h later, and RNA was extracted using a RNeasy Mini Kit (Qiagen) with an on-column DNase digestion. Sequencing libraries were generated using a TruSeq Stranded mRNA LT Sample Prep Kit (Illumina) and run on a NextSeq 500 using a NextSeq 500/550 High Output v2 Kit, 75 cycle (Illumina). AGO2–/– HCT116EGFP-miR-19 cells generated using PX330 were reconstituted with either empty pLJM1 vector (with EGFP previously resected), FH-AGO2-WT (AGO2WT), or FH-AGO2-5XA (AGO25XA). Biological triplicates for each cell line were seeded with 5.0 × 105 cells per well in six-well dishes. Cells were collected 48 h later, and RNA was extracted using a miRNeasy Mini Kit (Qiagen) with an on-column DNase digestion. Sequencing libraries were generated using a TruSeq Stranded Total RNA with Ribo-Zero Human/Mouse/Rat Low-throughput (LT) kit (Illumina) and run as performed in the previous RNA-seq experiment. Quality assessment of the RNA-seq data was done using the NGS-QC-Toolkit43 with default settings. Quality-filtered reads generated by the tool were then aligned to the human reference genome hg19 (for AGO2–/–, ANKRD52–/–, and ANKRD52–/–;CSNK1A1–/– RNA-seq experiments) or hg38 (for FH-AGO2 reconstitution experiments) using the TopHat2 (version 2.0.12) aligner44 using default settings. Read counts obtained from featureCounts45 were used as input for edgeR (version 3.8.6)46 for differential expression analysis. Genes with FDR ≤ 0.05 were regarded as differentially expressed for comparisons of each sample group. Cell culture, library preparation, and deep sequencing. AGO2–/– cells or AGO2–/– cells reconstituted with FH-AGO2WT or FH-AGO25XA via lentiviral expression (described above) were seeded in 15-cm dishes with five dishes per cell line at 1.0 × 107 cells per dish. Cells were cultured for 48 h and subsequently ultraviolet crosslinked at 400 mJ/cm2. Aliquots of 2.0 × 107 cells were then frozen at −80 °C. eCLIP was performed using the frozen samples as previously described37, using anti-Flag antibody for immunoprecipitations (F1804, Sigma). For each cell line, duplicate input and immunoprecipitation samples were prepared and sequenced. The RiL19 RNA adaptor (Supplementary Table 8) was used as the 3′ RNA linker for all samples. PAGE-purified DNA oligonucleotides were obtained from Sigma for the PCR library amplification step (Supplementary Table 8). PCR amplification was performed using between 11 and 15 cycles for all samples. Paired-end sequencing was performed on a NextSeq 500 using a NextSeq 500/550 High Output v2 Kit, 75 cycle (Illumina). Mapping deep sequencing reads. Adapters were trimmed from original reads using Cutadapt (version 1.9.1)47 with default settings. Next, the randomer sequence from the rand103Tr3 linker (Supplementary Table 8) was trimmed and recorded. TopHat2 (version 2.0.12)44 was used to align mate 2 to hg38. Only the uniquely mapped reads were retained. PCR duplicates were then removed using the randomer information with an in-house script. All reads remaining after PCR duplicate removal were regarded as usable reads and used for cluster calling. eCLIP cluster calling and annotation. eCLIP clusters were identified using a previously described method6 with the following modifications. Genome coverage by usable reads was determined at nucleotide resolution for each data set, and regions of continuous coverage greater than expected from a Poisson noise distribution were identified (P ≤ 0.001). For each region, read counts were obtained using Bedtools (version 2.17)48. If 50% of a read overlapped a region on the same strand, it was counted as a read covering that region. For each region, normalization to total usable reads was performed and a fold change between immunoprecipitation and input samples was calculated. Significant CLIP clusters in each data set were defined by (1) the presence of significantly greater coverage in the region than expected by chance on the basis of the Poisson distribution, and (2) log (fold change) of normalized reads in the cluster was ≥2 comparing immunoprecipitation to input. The final CLIP clusters for FH-AGO2WT and FH-AGO25XA were identified by first identifying significant clusters present in both experimental replicates. A region was considered to be present in both replicates if it occurred on the same strand and the replicate clusters overlapped by at least one-third of their total length. Significant clusters from both replicates were then merged to define the final cluster length. Lastly, all clusters identified in the AGO2–/– samples were subtracted to generate the final CLIP cluster calls (Supplementary Table 7). Clusters were annotated on the basis of their genomic locations (Ensembl GRCh38.85) if 55% of the cluster overlapped with a given genomic region. If a cluster was assigned to multiple annotations, the annotation was selected using the following priority: CDS exon > 3′ UTR > 5′ UTR > protein-coding gene intron > noncoding RNA exon > noncoding RNA intron > intergenic. Identification of active miRNA seed families and calculation of CLIP coverage at miRNA binding sites. Active miRNAs in HCT116 were identified using an approach similar to that described previously6 with the following modifications. The top 100 most highly expressed miRNAs in HCT116 cells were identified on the basis of a previously published small RNA sequencing experiment in this cell line49 and collapsed to 66 7-nucleotide seed families with identical sequence from nucleotides 2–8. Eight-nucleotide binding sites for these seeds, defined as in ref. 3, were identified in the 3′ UTRs of all expressed genes (FPKM > 0) using seqMap (version 1.0.12)50. The locations were then transformed to genomic coordinates and extended 10 nucleotides upstream and downstream to obtain a seed match region (excluding sites on exon–exon junctions). The numbers of crosslinking sites in these seed match regions for each miRNA seed family in FH-AGO2WT CLIP data were counted, normalized to the total usable reads in each replicate library, and averaged across replicates. To determine the significance cut-off, all possible 8-nucleotide sequences except for known miRNA seeds and those with four consecutive A, C, G, or T nucleotides were used to generate a null distribution. These background 8-nucleotide sequences were divided into 13 groups with 1,000 8-nucleotide sequences in the first 12 groups and 678 8-nucleotide sequences in the final group. CLIP crosslinking to each 8-nucleotide sequence in expressed 3′ UTRs was quantified as described above for actual miRNA seeds. An mRNA seed family was considered to be active in HCT116 cells if it obtained more crosslinking events than expected by chance, defined by the average number of crosslinking events from each of the 13 background 8-nucleotide groups above which P < 0.01. On the basis of this analysis, 15 active miRNA seed families were identified (representative miRNA: miR-423-5p, miR-17-5p, miR-200a-3p, miR-19a-3p, miR-23a-3p, miR-148a-3p, miR-221-3p, miR-125-5p, miR-182-5p, miR-21-5p, miR-30a-5p, miR-25-3p, let-7a-5p, miR-27a-3p, miR-24-3p). To quantify CLIP coverage of miRNA binding sites in FH-AGO2WT and FH-AGO25XA CLIP data (Fig. 6c), 8-, 7-, and 6-nucleotide binding sites, defined as in ref. 3, for all active miRNAs were identified within FH-AGO2WT CLIP clusters in 3′ UTRs using seqMap. Clusters with only a single type of binding site (8, 7, and 6 nucleotides) were identified. If an 8-nucleotide binding site was identified, this site was excluded from 7- or 6-nucleotide categories. Likewise, 7-nucleotide sites were excluded from the 6-nucleotide sites. Clusters were further filtered for those that were present in transcripts with FPKM > 0 in both FH-AGO2WT and FH-AGO25XA cell lines, yielding 228, 89, and 80 clusters containing 6, 7, or 8 nucleotides, respectively. For each cluster with a given type of binding site, CLIP coverage was calculated by determining the average number of CLIP reads in the cluster in each replicate normalized to the total number of reads in all clusters in each replicate, divided by FPKM of the transcript. The final reported CLIP coverage is the average of both replicates. To quantify CLIP coverage of miRNA binding sites in FH-AGO25XA-unique clusters versus FH-AGO2WT/FH-AGO25XA-common clusters (Extended Data Fig. 10e), 8-, 7-, and 6-nucleotide binding sites for all active miRNAs were identified within each class of CLIP cluster. Windows around each site were then extended 10 nucleotides upstream and downstream to obtain a seed match region. The numbers of crosslinking sites within these regions were counted and normalized to the total number of reads in clusters of each class (FH-AGO25XA-unique or FH-AGO2WT/FH-AGO25XA-common) to derive the CLIP coverage used to draw the CDF plots. CLIP coverage of FH-AGO25XA rescued versus non-rescued transcripts. Genes whose repression in AGO2–/– cells was rescued by FH-AGO25XA were defined by first identifying the genes that were significantly upregulated in AGO2–/– cells compared with parental HCT116 (FDR ≤ 0.05), then, among these genes, those that were significantly downregulated in FH-AGO25XA versus AGO2–/– (FDR ≤ 0.05). All other genes upregulated in AGO2–/– cells were considered not-rescued. The FH-AGO2WT CLIP coverage for each gene in these classes was calculated as the sum of all reads in CLIP clusters in a given 3′ UTR, normalized to total reads in all clusters, divided by the FPKM of the transcript. The final reported CLIP coverage (Extended Data Fig. 10d) is the average of both FH-AGO2WT replicates. mRNA half-life analysis. Half-lives of transcripts with FH-AGO25XA CLIP clusters in their 3′ UTRs were obtained from a previously published study38. Genes that had half-lives assigned to more than one RefSeq mRNA isoform were removed to avoid ambiguity. Genes in the top quartile of half-lives were defined as having a long half-life (n = 273) and genes in the bottom quartile of half-lives were defined as having a short half-life (n = 274). The total numbers of CLIP reads in clusters in a given 3′ UTR were obtained for each replicate, and edgeR (version 3.8.6)46 was used to calculate the normalized fold change of CLIP coverage comparing FH-AGO25XA with FH-AGO2WT (Extended Data Fig. 10f). All high-throughput sequencing data generated in the course of this study (CRISPR–Cas9 screens, RNA-seq, eCLIP) have been deposited in Gene Expression Omnibus under accession number GSE89946. All other data are available from the corresponding author upon reasonable request.
Murtas D.,U.S. National Institutes of Health |
Murtas D.,University of Cagliari |
Maric D.,Flow Cytometry Core Facility |
De Giorgi V.,U.S. National Institutes of Health |
And 19 more authors.
British Journal of Cancer | Year: 2013
Background:Several lines of evidence suggest a dichotomy between immune active and quiescent cancers, with the former associated with a good prognostic phenotype and better responsiveness to immunotherapy. Central to such dichotomy is the master regulator of the acute inflammatory process interferon regulatory factor (IRF)-1. However, it remains unknown whether the responsiveness of IRF-1 to cytokines is able to differentiate cancer immune phenotypes.Methods:IRF-1 activation was measured in 15 melanoma cell lines at basal level and after treatment with IFN-γ, TNF-α and a combination of both. Microarray analysis was used to compare transcriptional patterns between cell lines characterised by high or low IRF-1 activation.Results:We observed a strong positive correlation between IRF-1 activation at basal level and after IFN-γ and TNF-α treatment. Microarray demonstrated that three cell lines with low and three with high IRF-1 inducible translocation scores differed in the expression of 597 transcripts. Functional interpretation analysis showed mTOR and Wnt/β-cathenin as the top downregulated pathways in the cell lines with low inducible IRF-1 activation, suggesting that a low IRF-1 inducibility recapitulates a cancer phenotype already described in literature characterised by poor prognosis.Conclusion:Our findings support the central role of IRF-1 in influencing different tumour phenotypes. © 2013 Cancer Research UK. All rights reserved.
News Article | February 23, 2017
Current treatments for rheumatoid arthritis relieve the inflammation that leads to joint destruction, but the immunologic defect that triggers the inflammation persists to cause relapses, according to research conducted at NYU Langone Medical Center and the University of Pittsburgh. Known as autoantibodies and produced by the immune system's B cells, these defective molecules mistakenly attack the body's own proteins in an example of autoimmune disease. Now the results of a study just published in Arthritis & Rheumatology suggest that clinical trials for new rheumatoid arthritis (RA) drugs should shift from their sole focus on relieving inflammation to eliminating the B cells that produce these antibodies. "We have developed a test for measuring the underlying autoimmunity in rheumatoid arthritis patients that should be used to evaluate new treatment regimens," says senior author Gregg Silverman, MD, professor in the Departments of Medicine and Pathology at NYU Langone and co-director of its Musculoskeletal Center of Excellence. "We believe this provides a road to a cure for rheumatoid arthritis." Rheumatoid arthritis is a chronic inflammatory autoimmune disease that affects 1.5 million people in the United States. The current standard of care begins with methotrexate, a drug that reduces inflammation. It is often followed by drugs that block a molecule called tumor necrosis factor (TNF), which promotes inflammation. Both of these classes of drugs can blunt the swelling and inflammation associated with rheumatoid arthritis and at times even allow patients to go into clinical remission that requires continued treatment. But when patients halt these medications, symptoms generally flare up either sooner or later. According to Silverman, the reduction of inflammation does not directly reflect the autoimmune disease that causes rheumatoid arthritis. In the study, researchers focused on "memory" B cells, immune system cells that remember the initial errant immune encounter that recognized the body's own proteins as foreign. In rheumatoid arthritis, memory B cells secrete molecules called anti-citrullinated protein antibodies (ACPAs). Doctors currently confirm an RA diagnosis with a blood test that looks for ACPAs, which are present in 80 percent of RA patients. Silverman and his colleagues developed sensitive assays to detect a range of different autoantibodies present in the disease. The researchers then established a cell culture system to stimulate memory B cells, and used the assays to test what kind of antibodies the B cells produced. The researchers tested blood samples from RA patients and from healthy donors. They found high levels of APCA-secreting memory B cells in the blood of patients with these autoantibodies, but not in patients without autoantibodies or in the healthy volunteers. They then looked at patients who had achieved remission with either methotrexate or a TNF inhibitor. The researchers found that APCA levels were directly proportional to the recirculating memory B cells in the blood stream, confirming that current drug treatments do not affect the underlying autoimmunity in rheumatoid arthritis. The next step, Silverman says, is to conduct long-term prospective clinical trials of new RA drugs, using the team's new test to determine each drug's effect on autoimmunity. The current metrics for evaluating the effectiveness of new rheumatoid arthritis drugs remain focused on reducing inflammation but not curing the disease, he says. "We need to develop longer-term vision of how to improve the treatment of rheumatoid arthritis," Silverman says. "This new tool may show that agents that target other molecules or cells have advantages that were previously not considered now that we can better measure those effects." Silverman's co-authors are Adam J. Pelzek, Caroline Grönwall, PhD, Pamela Rosenthal, MD, and Jeffrey D. Greenberg, MD, at NYU Langone; Mandy McGeachy, PhD, and Larry Moreland, MD, at the University of Pittsburgh; and William F.C. Rigby, MD, at Dartmouth Medical School. This work was supported by the National Institutes of Health (grants R01AI090118, R01AI068063, R01-AR42455, N01-AR-4-2271, an American Recovery and Reinvestment Act supplement, and the NIAID/NIH: NYU School of Medicine-Immunology & Inflammation Training Grant (T32 AI100853)). Research conducted at the NYU Immune Monitoring Core and the NYU Flow Cytometry Core Facility was supported by NYU-HHC CTSI Grant UL1 TR000038, and the NYU Laura and Isaac Perlmutter Cancer Center support grant, P30CA016087 from the National Center for Advancing Translational Sciences.
Debbache J.,U.S. National Institutes of Health |
Zaidi M.R.,U.S. National Cancer Institute |
Davis S.,NCI Inc |
Guo T.,U.S. National Cancer Institute |
And 8 more authors.
Genetics | Year: 2012
The microphthalmia-associated transcription factor (MITF) is a basic helix-loop-helix leucine zipper protein that plays major roles in the development and physiology of vertebrate melanocytes and melanoma cells. It is regulated by post-translational modifications, including phosphorylation at serine 73, which based on in vitro experiments imparts on MITF an increased transcriptional activity paired with a decreased stability. Serine 73 is encoded by the alternatively spliced exon 2B, which is preferentially skipped in mice carrying a targeted serine-73-to-alanine mutation. Here, we measured the relative abundance of exon 2B+ and exon 2B- RNAs in freshly isolated and FACS-sorted wild-type melanoblasts and melanocytes and generated a series of knock-in mice allowing forced incorporation of either alanine, aspartate, or wild-type serine at position 73. None of these knock-in alleles, however, creates a striking pigmentation phenotype on its own, but differences between them can be revealed either by a general reduction of Mitf transcript levels or in heteroallelic combinations with extant Mitf mutations. In fact, compared with straight serine-73 knock-in mice with their relative reduction of 2B+ Mitf, forced incorporation of alanine 73 leads to greater increases in MITF protein levels, melanoblast and melanocyte numbers, and extent of pigmentation in particular allelic combinations. These results underscore, in vivo, the importance of the link between alternative splicing and post-translational modifications and may bear on the recent observation that exon 2B skipping can be found in metastatic melanoma. © 2012 by the Genetics Society of America.
Blake J.,Genomics Core Facility |
Riddell A.,Flow Cytometry Core Facility |
Riddell A.,University of Cambridge |
Theiss S.,University of Heidelberg |
And 10 more authors.
PLoS ONE | Year: 2014
Balanced chromosome abnormalities (BCAs) occur at a high frequency in healthy and diseased individuals, but cost-efficient strategies to identify BCAs and evaluate whether they contribute to a phenotype have not yet become widespread. Here we apply genome-wide mate-pair library sequencing to characterize structural variation in a patient with unclear neurodevelopmental disease (NDD) and complex de novo BCAs at the karyotype level. Nucleotide-level characterization of the clinically described BCA breakpoints revealed disruption of at least three NDD candidate genes (LINC00299, NUP205, PSMD14) that gave rise to abnormal mRNAs and could be assumed as disease-causing. However, unbiased genome-wide analysis of the sequencing data for cryptic structural variation was key to reveal an additional submicroscopic inversion that truncates the schizophrenia- and bipolar disorder-associated brain transcription factor ZNF804A as an equally likely NDD-driving gene. Deep sequencing of fluorescent-sorted wild-type and derivative chromosomes confirmed the clinically undetected BCA. Moreover, deep sequencing further validated a high accuracy of mate-pair library sequencing to detect structural variants larger than 10 kB, proposing that this approach is powerful for clinical-grade genome-wide structural variant detection. Our study supports previous evidence for a role of ZNF804A in NDD and highlights the need for a more comprehensive assessment of structural variation in karyotypically abnormal individuals and patients with neurocognitive disease to avoid diagnostic deception. © 2014 Blake et al.
Riddell A.,Flow Cytometry Core Facility |
Riddell A.,Cambridge Stem Cell Institute |
Gardner R.,Instituto Gulbenkian Of Ciencia |
Perez-Gonzalez A.,Flow Cytometry Core Facility |
And 3 more authors.
Methods | Year: 2015
Sorting performance can be evaluated with regard to Purity, Yield and/or Recovery of the sorted fraction. Purity is a check on the quality of the sample and the sort decisions made by the instrument. Recovery and Yield definitions vary with some authors regarding both as how efficient the instrument is at sorting the target particles from the original sample, others distinguishing Recovery from Yield, where the former is used to describe the accuracy of the instrument's sort count. Yield and Recovery are often neglected, mostly due to difficulties in their measurement. Purity of the sort product is often cited alone but is not sufficient to evaluate sorting performance. All of these three performance metrics require re-sampling of the sorted fraction. But, unlike Purity, calculating Yield and/or Recovery calls for the absolute counting of particles in the sorted fraction, which may not be feasible, particularly when dealing with rare populations and precious samples. In addition, the counting process itself involves large errors.Here we describe a new metric for evaluating instrument sort Recovery, defined as the number of particles sorted relative to the number of original particles to be sorted. This calculation requires only measuring the ratios of target and non-target populations in the original pre-sort sample and in the waste stream or center stream catch (CSC), avoiding re-sampling the sorted fraction and absolute counting. We called this new metric Rmax, since it corresponds to the maximum expected Recovery for a particular set of instrument parameters. Rmax is ideal to evaluate and troubleshoot the optimum drop-charge delay of the sorter, or any instrument related failures that will affect sort performance. It can be used as a daily quality control check but can be particularly useful to assess instrument performance before single-cell sorting experiments. Because we do not perturb the sort fraction we can calculate Rmax during the sort process, being especially valuable to check instrument performance during rare population sorts. © 2015 The Authors.
PubMed | Flow Cytometry Core Facility, Instituto Gulbenkian Of Ciencia, ETH Zurich, Flow Cytometry Unit and Cambridge Stem Cell Institute
Type: | Journal: Methods (San Diego, Calif.) | Year: 2015
Sorting performance can be evaluated with regard to Purity, Yield and/or Recovery of the sorted fraction. Purity is a check on the quality of the sample and the sort decisions made by the instrument. Recovery and Yield definitions vary with some authors regarding both as how efficient the instrument is at sorting the target particles from the original sample, others distinguishing Recovery from Yield, where the former is used to describe the accuracy of the instruments sort count. Yield and Recovery are often neglected, mostly due to difficulties in their measurement. Purity of the sort product is often cited alone but is not sufficient to evaluate sorting performance. All of these three performance metrics require re-sampling of the sorted fraction. But, unlike Purity, calculating Yield and/or Recovery calls for the absolute counting of particles in the sorted fraction, which may not be feasible, particularly when dealing with rare populations and precious samples. In addition, the counting process itself involves large errors. Here we describe a new metric for evaluating instrument sort Recovery, defined as the number of particles sorted relative to the number of original particles to be sorted. This calculation requires only measuring the ratios of target and non-target populations in the original pre-sort sample and in the waste stream or center stream catch (CSC), avoiding re-sampling the sorted fraction and absolute counting. We called this new metric Rmax, since it corresponds to the maximum expected Recovery for a particular set of instrument parameters. Rmax is ideal to evaluate and troubleshoot the optimum drop-charge delay of the sorter, or any instrument related failures that will affect sort performance. It can be used as a daily quality control check but can be particularly useful to assess instrument performance before single-cell sorting experiments. Because we do not perturb the sort fraction we can calculate Rmax during the sort process, being especially valuable to check instrument performance during rare population sorts.