Massively parallel digital transcriptional profiling of single cells methods
Aim. Evidence-backed execution summary for Massively parallel digital transcriptional profiling of single cells methods from Massively parallel digital transcriptional profiling of single cells.
Show snapshot details
On this page
This experiment, in seven questions
Jump straight to the part of the recipe you need. Data and provenance labels stay close to the action they support.
Shopping and prep list
What do I need before I start?
Biological model pending
Subject model for the experiment.
- Use
- confirm full cohort details in the source paper
Chimerism assay
reagent used in the protocol.
- Use
- PowerPlex 16 System (Promega) was used in conjunction with an Applied Biosystems (Life Technologies) 3130xl Genetic Analyzer. Donor BMMCs were used as the reference baseline.
Technical demonstration with cell lines and synthetic RNAs
reagent used in the protocol.
- Use
- The ERCC experiments also allowed us to estimate the relative proportion of biological and technical variation. Since ERCCs are in solution, they do not introduce biological variation related to differences in cell size, RNA content or transcriptional activity. Thus, technical variation is the only source of variati...
Single-cell analysis of transplant bone marrow samples
reagent used in the protocol.
- Use
- Single-cell RNA-seq libraries were generated from cryopreserved bone marrow mononuclear cell (BMMC) samples obtained from two patients before and after undergoing HSCT for AML (AML027 and AML035) ( ). Since HSCT samples are fragile, cells were carefully washed in PBS with 20% fetal bovine serum (FBS) before loading...
Cell lines and transplant patient samples
reagent used in the protocol.
- Use
- Jurkat (ATCC TIB-152), 293T (ATCC CRL-11268) and 3T3 (ATCC CRL-1658) cells were acquired from ATCC and cultured according to ATCC guidelines. Fresh PBMCs, frozen PBMCs and BMMCs were purchased from ALLCELLS. Frozen PBMCs from Donor A were made from fresh PBMCs from Donor A by mixing 1e 6 cells in freezing medium (15...
Cell lines and transplant patient samples
reagent used in the protocol.
- Use
- Bone marrow aspirates were obtained for standard clinical testing 20-30 days before transplant and serially post-transplanted according to the treatment protocol. Bone marrow aspirate aliquots were processed within 2 h of the draw. The BMMCs were isolated using centrifugation through a Ficoll gradient (H...
Estimation of RNA content per cell
reagent used in the protocol.
- Use
- The amount of RNA per cell type was determined by quantifying (Qubit; Invitrogen) RNA extracted (Maxwell RSC simplyRNA Cells Kit) from several different known numbers of cells.
Cell preparation
reagent used in the protocol.
- Use
- Fresh cells were harvested, washed with 1 × PBS and resuspended at 1 × 10 6 cells per ml in 1 × PBS and 0.04% bovine serum albumin. Fresh PBMCs were frozen at 10 × by resuspending PBMCs in DMEM+40% FBS+10% DMSO, freezing to -by °C in a CoolCell® FTS30 (BioCision) and then pl...
Cell preparation
reagent used in the protocol.
- Use
- Frozen cell vials from ALLCELLS and transplant studies were rapidly thawed in a 37 °C water bath for ∼2 min. Vials were removed when a tiny ice crystal was left. Thawed PBMCs were washed twice in the medium and then resuspended in 1 × PBS and 0.04% bovine serum albumin at room temperature...
Cell capture efficiency calculation
The efficiency is calculated by taking the ratio of the number of cells detected by sequencing versus the number of cells loaded into the chip. The latter is determined from (volume added × input concentration of cells). The input concentration of cells was determined using a Countess II Automated Cell Counter...
- Use
- The efficiency is calculated by taking the ratio of the number of cells detected by sequencing versus the number of cells loaded into the chip. The latter is determined from (volume added × input concentration of cells). The input concentration of cells was determined using a Countess II Automated Cell Counter...
Chimerism assay
PowerPlex 16 System (Promega) was used in conjunction with an Applied Biosystems (Life Technologies) 3130xl Genetic Analyzer. Donor BMMCs were used as the reference baseline.
- Use
- PowerPlex 16 System (Promega) was used in conjunction with an Applied Biosystems (Life Technologies) 3130xl Genetic Analyzer. Donor BMMCs were used as the reference baseline.
Comparison between fresh and frozen PBMCs
The sequencing data of 68k fresh PBMCs and 3k frozen PBMCs were down-sampled such that each sample has ∼14k confidently mapped reads per cell. Only genes that are detected in at least one cell were included for the comparison, which uses the mean of each gene across all cells.
- Use
- The sequencing data of 68k fresh PBMCs and 3k frozen PBMCs were down-sampled such that each sample has ∼14k confidently mapped reads per cell. Only genes that are detected in at least one cell were included for the comparison, which uses the mean of each gene across all cells.
Technical demonstration with cell lines and synthetic RNAs
To assess the technical performance of our system, we loaded a mixture of ∼1,200 human (293T) and ∼1,200 mouse (3T3) cells and sequenced the library on the Illumina NextSeq 500 to yield ∼100k reads per cell. Sequencing data were processed by CellRanger (Supplementary Methods and ). Briefly, 98 nucl...
- Use
- To assess the technical performance of our system, we loaded a mixture of ∼1,200 human (293T) and ∼1,200 mouse (3T3) cells and sequenced the library on the Illumina NextSeq 500 to yield ∼100k reads per cell. Sequencing data were processed by CellRanger (Supplementary Methods and ). Briefly, 98 nucl...
Technical demonstration with cell lines and synthetic RNAs
Based on the distribution of total UMI counts for each barcode (Supplementary Methods), we estimated that 1,012 GEMs contained cells, of which 482 and 538 contained reads that mapped primarily to the human and mouse transcriptome, respectively (and will be referred to as human and mouse GEMs) ( ). Greater than eight...
- Use
- Based on the distribution of total UMI counts for each barcode (Supplementary Methods), we estimated that 1,012 GEMs contained cells, of which 482 and 538 contained reads that mapped primarily to the human and mouse transcriptome, respectively (and will be referred to as human and mouse GEMs) ( ). Greater than eight...
Technical demonstration with cell lines and synthetic RNAs
We also directly measured cDNA conversion rate by loading External RNA Controls Consortium (ERCC) synthetic RNAs into GEMs in place of cells. We found that mean UMI counts from sequencing was highly correlated ( r =0.96) with molecule counts calculated from the loading concentration of ERCC ( and ). Furthermore, we...
- Use
- We also directly measured cDNA conversion rate by loading External RNA Controls Consortium (ERCC) synthetic RNAs into GEMs in place of cells. We found that mean UMI counts from sequencing was highly correlated ( r =0.96) with molecule counts calculated from the loading concentration of ERCC ( and ). Furthermore, we...
Detection of individual populations in mixed samples
We tested the ability of the system to accurately detect heterogeneous populations by mixing two cell lines, 293T and Jurkat cells, at different ratios ( ). We performed principal component analysis (PCA) on UMI counts from all detected genes after pooling all the samples ( ). In the sample where an equal number of...
- Use
- We tested the ability of the system to accurately detect heterogeneous populations by mixing two cell lines, 293T and Jurkat cells, at different ratios ( ). We performed principal component analysis (PCA) on UMI counts from all detected genes after pooling all the samples ( ). In the sample where an equal number of...
Subpopulation discovery from a large immune population
The GemCode single-cell technology can also be used for scRNA-seq of primary cells. To study immune populations within PBMCs, we obtained fresh PBMCs from a healthy donor (Donor A). 8-9k cells were captured from each of 8 channels and pooled to obtain ∼68k cells. Data from multiple sequencing runs were m...
- Use
- The GemCode single-cell technology can also be used for scRNA-seq of primary cells. To study immune populations within PBMCs, we obtained fresh PBMCs from a healthy donor (Donor A). 8-9k cells were captured from each of 8 channels and pooled to obtain ∼68k cells. Data from multiple sequencing runs were m...
Before you run
What should be confirmed before execution?
First confirmation
Species or subject information is missing.
Confirm before execution
Equipment is listed but no product mappings are linked.
Confirm before execution
This page is backed by a publishable Replication Data Ledger package with zero critical source-verification issues.
Confirm before execution
Open the source paper before finalizing run-specific details.
Procurement checkpoint
Use source-stated vendors where present. Treat mapped products as sourcing options unless the page marks an exact source match.
Open quote workflowStep-by-step procedure
What do I do, in order?
Single-cell RNA profiling of cryopreserved PBMCs
To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens, we froze the remaining fresh PBMCs from Donor A, and made a scRNA-seq library from gently thawed cells 3 weeks later where ∼3k cells were recovered (Supplementary Methods). The two data sets (fresh and frozen) showed a high similarity between their average gene expression ( r =0.96; Supplementary Methods and ). Fifty-seven genes showed twofold upregulation in the frozen sample, with ∼50% being ribosomal protein genes, and the rest not enriched in any pathways ( ). In addition, the number of genes and UMI counts detected from fresh and frozen PBMCs was very similar ( P =0.8 and 0.1, respectively), suggesting that the conversion efficiency of the system is not compromised when profiling frozen cells (...
Genotype-based method to detect individual cell populations
Next, we applied the GemCode technology to study host and donor cell chimerism in an allogeneic hematopoietic stem cell transplant (HSCT) setting. Following a stem cell transplant, it is important to monitor the proportion of donor and host cells in major cell lineages to ensure complete engraftment and as a sensitive means of detecting impending relapse. Currently, the amount of host and donor chimerism is often measured from flow-sorted cell populations using PCR assays with a panel of SNV-specific primers. Current clinical chimerism tests have a number of limitations, namely (1) the flow-sorted cell populations are limited by cell surface markers, (2) only populations with sufficient cell counts can be used for PCR assays and (3) they are not intended for the detection of minimal residual disease. Here we present a simple method that addresses these limitations, resolves host and...
Genotype-based method to detect individual cell populations
While previous studies have used existing SNVs from DNA sequencing or large-scale copy number changes in the transcriptome data to distinguish cells by genotype, these methods cannot be applied to transplant samples where donor and host genotype is not known a priori, and when donor and host are closely matched in genotype. To address these limitations, we first developed a method to infer the relative presence of host and donor genotypes in a mixed population based on SNVs directly predicted from the transcriptome data. The method identifies SNVs and infers a genotype at each SNV. It then classifies cells based on their genotypes across all SNVs (Supplementary Methods).
Single-cell analysis of transplant bone marrow samples
Single-cell RNA-seq libraries were generated from cryopreserved bone marrow mononuclear cell (BMMC) samples obtained from two patients before and after undergoing HSCT for AML (AML027 and AML035) ( ). Since HSCT samples are fragile, cells were carefully washed in PBS with 20% fetal bovine serum (FBS) before loading them into chips. Relative to BMMCs from two healthy controls, we found the median number of UMI counts per cell to be 3-5 times higher in AML samples at ∼15k reads per cell, suggesting their vastly abnormal transcriptional programs ( ). Approximately 35 and 60 SNVs per cell were detected from AML027 and AML035 pre-transplant samples, respectively ( and ). Our SNV analysis detected the presence of two genotypes in the post-transplant sample of AML027: one at 13.8% and one at 86.2% ( ). As expected, there was no evidence of multiple genotype groups in the pre-tran...
Single-cell analysis of transplant bone marrow samples
SNV and scRNA-seq analyses enable subpopulation comparison between individuals within and across multiple samples. We applied these analyses on BMMC scRNA-seq data from healthy controls and AML patients (Supplementary Methods), and observed subpopulation differences in AML patients after HSCT. First, while T cells dominate the healthy BMMCs and donor cells of the AML027 post-transplant sample as expected, erythroids constitute the largest population among AML samples ( ). Different sets of progenitor and differentiation markers (for example, CD34, GATA1, CD71 and HBA1 ) were detected among the erythroids, indicating populations at various stages of erythroid development (Supplementary Methods and ). AML027 showed the highest level of erythroid cells (>80%, consist of mostly mature erythroids) before transplant, consistent with the erythroleukaemia diagnosis of AML027 ( ). In contras...
Cell lines and transplant patient samples
Jurkat (ATCC TIB-152), 293T (ATCC CRL-11268) and 3T3 (ATCC CRL-1658) cells were acquired from ATCC and cultured according to ATCC guidelines. Fresh PBMCs, frozen PBMCs and BMMCs were purchased from ALLCELLS. Frozen PBMCs from Donor A were made from fresh PBMCs from Donor A by mixing 1e 6 cells in freezing medium (15% dimethylsulphoxide (DMSO) in Iscove's modified Dulbecco's media containing 20% FBS) gently, and chilled in CoolCell FTS30 (BioCision) in -80 °C for at least 4 h before transferring to liquid nitrogen for storage for 3 weeks.
Cell lines and transplant patient samples
The Institutional Review Board at the Fred Hutchinson Cancer Research Center approved the study on transplant samples. The procedures followed were in accordance with the Declaration of Helsinki of 1975 and the Common Rule. Samples were obtained after patients had provided written informed consent on molecular analyses. We identified patients with AML undergoing allogeneic hematopoietic stem cell transplant at the Fred Hutchinson Cancer Research Center. The diagnosis of AML was established according to the revised criteria of the World Health Organization.
Cell lines and transplant patient samples
Bone marrow aspirates were obtained for standard clinical testing 20-30 days before transplant and serially post-transplanted according to the treatment protocol. Bone marrow aspirate aliquots were processed within 2 h of the draw. The BMMCs were isolated using centrifugation through a Ficoll gradient (Histopaque-1077; Sigma Life Science, St Louis, MO, USA). The BMMCs were collected from the serum-Ficoll interface with a disposable Pasteur pipette and transferred to the 50 ml conical tube with 2% patient serum in 1 × PBS. The BMMCs were counted using a haemacytometer and viability was assessed using Trypan blue. The BMMCs were resuspended in 90% FBS, 10% DMSO freezing media and frozen using a Thermo Scientific Nalgene Mr Frosty (Thermo Scientific) in a -80 °C freezer for 24 h before being transferred to liquid nitrogen for long-term storage.
Measurement outputs
What raw and processed outputs should exist?
All relevant data are available from the authors. Single-cell RNA-seq data have been deposited in the Short Read Archive under accession number SRP073767. Data are also availabl...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
To assess the technical performance of our system, we loaded a mixture of ∼1,200 human (293T) and ∼1,200 mouse (3T3) cells and sequenced the library on the Illumina...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
We also directly measured cDNA conversion rate by loading External RNA Controls Consortium (ERCC) synthetic RNAs into GEMs in place of cells. We found that mean UMI counts from...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
The ERCC experiments also allowed us to estimate the relative proportion of biological and technical variation. Since ERCCs are in solution, they do not introduce biological var...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
Analysis plan
How should the outputs become interpretable results?
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
inferred from protocolPreprocessing / cleaning
To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens, we froze the remaining fresh PBMCs from Donor A, and made a scRNA-seq library from gently thawed cel...
from paperScoring or quantification
Quantify the primary readouts for this experiment: All relevant data are available from the authors. Single-cell RNA-seq data have been deposited in the Short Read Archive under accession number SRP073767. Data are also availabl...; To assess the technical performance of our system, we loaded a mixture of ∼1,200 human (293T) and ∼1,200 mouse (3T3) cells and sequenced the library on the Illumina...; We also directly measured cDNA conversion rate by loading External RNA Controls Consortium (ERCC) synthetic RNAs into GEMs in place of cells. We found that mean UMI counts from...; The ERCC experiments also allowed us to estimate the relative proportion of biological and technical variation. Since ERCCs are in solution, they do not introduce biological var....
from paperStatistical comparison
To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens,...; In the mouse and human mixing experiments, multiplet rate was defined as twice the rate of cell barcodes with significant UMI counts from both mouse and human, where top 1% of U...; The gene-cell-barcode matrix of 68k PBMCs was log-transformed as an input to Seurat. The top 469 most variable genes selected by Seurat were used to compute the PCs. The first 2...
from paperReporting output
Report representative outputs alongside summary comparisons for All relevant data are available from the authors. Single-cell RNA-seq data have been deposited in the Short Read Archive under accession number SRP073767. Data are also availabl..., To assess the technical performance of our system, we loaded a mixture of ∼1,200 human (293T) and ∼1,200 mouse (3T3) cells and sequenced the library on the Illumina..., We also directly measured cDNA conversion rate by loading External RNA Controls Consortium (ERCC) synthetic RNAs into GEMs in place of cells. We found that mean UMI counts from..., The ERCC experiments also allowed us to estimate the relative proportion of biological and technical variation. Since ERCCs are in solution, they do not introduce biological var....
inferred from protocolStructured statistical methods
To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens,...; In the mouse and human mixing experiments, multiplet rate was defined as twice the rate of cell barcodes with significant UMI counts from both mouse and human, where top 1% of U...; The gene-cell-barcode matrix of 68k PBMCs was log-transformed as an input to Seurat. The top 469 most variable genes selected by Seurat were used to compute the PCs. The first 2...
source structuredSource and audit
What supports the facts on this page?
Evidence quotes (8)
To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens, we froze the remaining fresh PBMCs from Donor A, and made a scRNA-seq library from gently thawed cells 3 weeks later where ∼3k cells were recovered (Supplementary Methods). The two data sets (fresh and frozen) showed a high similarity between their average gene expression ( r =0.96; Supplementary Methods and ). Fifty-seven genes showed twofold upregulation in the frozen sample, with ∼50% being ribosomal protein genes, and the rest not enriched in any pathways ( ). In addition, the number of genes and UMI counts detected from fresh and frozen PBMCs was very similar ( P =0.8 and 0.1, respectively), suggesting that the conversion efficiency of the system is not compromised when profiling frozen cells ( ). Furthermore, subpopulations were detected from frozen PBMCs at a similar proportion to that of fresh PBMCs, demonstrating the applicability of our method on frozen samples (Supplementary Methods and ).
Next, we applied the GemCode technology to study host and donor cell chimerism in an allogeneic hematopoietic stem cell transplant (HSCT) setting. Following a stem cell transplant, it is important to monitor the proportion of donor and host cells in major cell lineages to ensure complete engraftment and as a sensitive means of detecting impending relapse. Currently, the amount of host and donor chimerism is often measured from flow-sorted cell populations using PCR assays with a panel of SNV-specific primers. Current clinical chimerism tests have a number of limitations, namely (1) the flow-sorted cell populations are limited by cell surface markers, (2) only populations with sufficient cell counts can be used for PCR assays and (3) they are not intended for the detection of minimal residual disease. Here we present a simple method that addresses these limitations, resolves host and donor chimerism at single-cell resolution and enables extensive characterization of cell subtypes by integrating scRNA-seq with de novo SNV calling.
While previous studies have used existing SNVs from DNA sequencing or large-scale copy number changes in the transcriptome data to distinguish cells by genotype, these methods cannot be applied to transplant samples where donor and host genotype is not known a priori, and when donor and host are closely matched in genotype. To address these limitations, we first developed a method to infer the relative presence of host and donor genotypes in a mixed population based on SNVs directly predicted from the transcriptome data. The method identifies SNVs and infers a genotype at each SNV. It then classifies cells based on their genotypes across all SNVs (Supplementary Methods).
Single-cell RNA-seq libraries were generated from cryopreserved bone marrow mononuclear cell (BMMC) samples obtained from two patients before and after undergoing HSCT for AML (AML027 and AML035) ( ). Since HSCT samples are fragile, cells were carefully washed in PBS with 20% fetal bovine serum (FBS) before loading them into chips. Relative to BMMCs from two healthy controls, we found the median number of UMI counts per cell to be 3-5 times higher in AML samples at ∼15k reads per cell, suggesting their vastly abnormal transcriptional programs ( ). Approximately 35 and 60 SNVs per cell were detected from AML027 and AML035 pre-transplant samples, respectively ( and ). Our SNV analysis detected the presence of two genotypes in the post-transplant sample of AML027: one at 13.8% and one at 86.2% ( ). As expected, there was no evidence of multiple genotype groups in the pre-transplant host sample. We compared the major and minor inferred genotypes present in the post-transplant sample to the genotype found in the host cells. The major inferred genotype in the post-transplant sample was 97% similar to that inferred from the host sample, while the minor inferred genotype was...
SNV and scRNA-seq analyses enable subpopulation comparison between individuals within and across multiple samples. We applied these analyses on BMMC scRNA-seq data from healthy controls and AML patients (Supplementary Methods), and observed subpopulation differences in AML patients after HSCT. First, while T cells dominate the healthy BMMCs and donor cells of the AML027 post-transplant sample as expected, erythroids constitute the largest population among AML samples ( ). Different sets of progenitor and differentiation markers (for example, CD34, GATA1, CD71 and HBA1 ) were detected among the erythroids, indicating populations at various stages of erythroid development (Supplementary Methods and ). AML027 showed the highest level of erythroid cells (>80%, consist of mostly mature erythroids) before transplant, consistent with the erythroleukaemia diagnosis of AML027 ( ). In contrast, after transplant, AML027 showed the highest level of blast cells and immature erythroids ( CD34+, GATA1 +), consistent with the relapse diagnosis and return of the malignant host AML ( ). These observations would have been difficult to make with FACS analysis, with limited number of markers for...
Jurkat (ATCC TIB-152), 293T (ATCC CRL-11268) and 3T3 (ATCC CRL-1658) cells were acquired from ATCC and cultured according to ATCC guidelines. Fresh PBMCs, frozen PBMCs and BMMCs were purchased from ALLCELLS. Frozen PBMCs from Donor A were made from fresh PBMCs from Donor A by mixing 1e 6 cells in freezing medium (15% dimethylsulphoxide (DMSO) in Iscove's modified Dulbecco's media containing 20% FBS) gently, and chilled in CoolCell FTS30 (BioCision) in -80 °C for at least 4 h before transferring to liquid nitrogen for storage for 3 weeks.
The Institutional Review Board at the Fred Hutchinson Cancer Research Center approved the study on transplant samples. The procedures followed were in accordance with the Declaration of Helsinki of 1975 and the Common Rule. Samples were obtained after patients had provided written informed consent on molecular analyses. We identified patients with AML undergoing allogeneic hematopoietic stem cell transplant at the Fred Hutchinson Cancer Research Center. The diagnosis of AML was established according to the revised criteria of the World Health Organization.
Bone marrow aspirates were obtained for standard clinical testing 20-30 days before transplant and serially post-transplanted according to the treatment protocol. Bone marrow aspirate aliquots were processed within 2 h of the draw. The BMMCs were isolated using centrifugation through a Ficoll gradient (Histopaque-1077; Sigma Life Science, St Louis, MO, USA). The BMMCs were collected from the serum-Ficoll interface with a disposable Pasteur pipette and transferred to the 50 ml conical tube with 2% patient serum in 1 × PBS. The BMMCs were counted using a haemacytometer and viability was assessed using Trypan blue. The BMMCs were resuspended in 90% FBS, 10% DMSO freezing media and frozen using a Thermo Scientific Nalgene Mr Frosty (Thermo Scientific) in a -80 °C freezer for 24 h before being transferred to liquid nitrogen for long-term storage.
Machine-readable layer
[
{
"@context": "https://schema.org",
"@type": "HowTo",
"name": "Massively parallel digital transcriptional profiling of single cells methods",
"description": "Evidence-backed execution summary for Massively parallel digital transcriptional profiling of single cells methods from Massively parallel digital transcriptional profiling of single cells.",
"totalTime": "PT28800M",
"step": [
{
"@type": "HowToStep",
"position": 1,
"name": "Single-cell RNA profiling of cryopreserved PBMCs",
"text": "To determine the effect that a freeze-thaw might have on gene expression and thus on the ability of our scRNA-seq pipeline to classify cell type in frozen repository specimens, we froze the remaining fresh PBMCs from Donor A, and made a scRNA-seq library from gently thawed cells 3 weeks later where ∼3k cells were recovered (Supplementary Methods). The two data sets (fresh and frozen) showed a high similarity between their average gene expression ( r =0.96; Supplementary Methods and ). Fifty-seven genes showed twofold upregulation in the frozen sample, with ∼50% being ribosomal protein genes, and the rest not enriched in any pathways ( ). In addition, the number of genes and UMI counts detected from fresh and frozen PBMCs was very similar ( P =0.8 and 0.1, respectively), suggesting that the conversion efficiency of the system is not compromised when profiling frozen cells (..."
},
{
"@type": "HowToStep",
"position": 2,
"name": "Genotype-based method to detect individual cell populations",
"text": "Next, we applied the GemCode technology to study host and donor cell chimerism in an allogeneic hematopoietic stem cell transplant (HSCT) setting. Following a stem cell transplant, it is important to monitor the proportion of donor and host cells in major cell lineages to ensure complete engraftment and as a sensitive means of detecting impending relapse. Currently, the amount of host and donor chimerism is often measured from flow-sorted cell populations using PCR assays with a panel of SNV-specific primers. Current clinical chimerism tests have a number of limitations, namely (1) the flow-sorted cell populations are limited by cell surface markers, (2) only populations with sufficient cell counts can be used for PCR assays and (3) they are not intended for the detection of minimal residual disease. Here we present a simple method that addresses these limitations, resolves host and..."
},
{
"@type": "HowToStep",
"position": 3,
"name": "Genotype-based method to detect individual cell populations",
"text": "While previous studies have used existing SNVs from DNA sequencing or large-scale copy number changes in the transcriptome data to distinguish cells by genotype, these methods cannot be applied to transplant samples where donor and host genotype is not known a priori, and when donor and host are closely matched in genotype. To address these limitations, we first developed a method to infer the relative presence of host and donor genotypes in a mixed population based on SNVs directly predicted from the transcriptome data. The method identifies SNVs and infers a genotype at each SNV. It then classifies cells based on their genotypes across all SNVs (Supplementary Methods)."
},
{
"@type": "HowToStep",
"position": 4,
"name": "Single-cell analysis of transplant bone marrow samples",
"text": "Single-cell RNA-seq libraries were generated from cryopreserved bone marrow mononuclear cell (BMMC) samples obtained from two patients before and after undergoing HSCT for AML (AML027 and AML035) ( ). Since HSCT samples are fragile, cells were carefully washed in PBS with 20% fetal bovine serum (FBS) before loading them into chips. Relative to BMMCs from two healthy controls, we found the median number of UMI counts per cell to be 3-5 times higher in AML samples at ∼15k reads per cell, suggesting their vastly abnormal transcriptional programs ( ). Approximately 35 and 60 SNVs per cell were detected from AML027 and AML035 pre-transplant samples, respectively ( and ). Our SNV analysis detected the presence of two genotypes in the post-transplant sample of AML027: one at 13.8% and one at 86.2% ( ). As expected, there was no evidence of multiple genotype groups in the pre-tran..."
},
{
"@type": "HowToStep",
"position": 5,
"name": "Single-cell analysis of transplant bone marrow samples",
"text": "SNV and scRNA-seq analyses enable subpopulation comparison between individuals within and across multiple samples. We applied these analyses on BMMC scRNA-seq data from healthy controls and AML patients (Supplementary Methods), and observed subpopulation differences in AML patients after HSCT. First, while T cells dominate the healthy BMMCs and donor cells of the AML027 post-transplant sample as expected, erythroids constitute the largest population among AML samples ( ). Different sets of progenitor and differentiation markers (for example, CD34, GATA1, CD71 and HBA1 ) were detected among the erythroids, indicating populations at various stages of erythroid development (Supplementary Methods and ). AML027 showed the highest level of erythroid cells (>80%, consist of mostly mature erythroids) before transplant, consistent with the erythroleukaemia diagnosis of AML027 ( ). In contras..."
},
{
"@type": "HowToStep",
"position": 6,
"name": "Cell lines and transplant patient samples",
"text": "Jurkat (ATCC TIB-152), 293T (ATCC CRL-11268) and 3T3 (ATCC CRL-1658) cells were acquired from ATCC and cultured according to ATCC guidelines. Fresh PBMCs, frozen PBMCs and BMMCs were purchased from ALLCELLS. Frozen PBMCs from Donor A were made from fresh PBMCs from Donor A by mixing 1e 6 cells in freezing medium (15% dimethylsulphoxide (DMSO) in Iscove's modified Dulbecco's media containing 20% FBS) gently, and chilled in CoolCell FTS30 (BioCision) in -80 °C for at least 4 h before transferring to liquid nitrogen for storage for 3 weeks."
},
{
"@type": "HowToStep",
"position": 7,
"name": "Cell lines and transplant patient samples",
"text": "The Institutional Review Board at the Fred Hutchinson Cancer Research Center approved the study on transplant samples. The procedures followed were in accordance with the Declaration of Helsinki of 1975 and the Common Rule. Samples were obtained after patients had provided written informed consent on molecular analyses. We identified patients with AML undergoing allogeneic hematopoietic stem cell transplant at the Fred Hutchinson Cancer Research Center. The diagnosis of AML was established according to the revised criteria of the World Health Organization."
},
{
"@type": "HowToStep",
"position": 8,
"name": "Cell lines and transplant patient samples",
"text": "Bone marrow aspirates were obtained for standard clinical testing 20-30 days before transplant and serially post-transplanted according to the treatment protocol. Bone marrow aspirate aliquots were processed within 2 h of the draw. The BMMCs were isolated using centrifugation through a Ficoll gradient (Histopaque-1077; Sigma Life Science, St Louis, MO, USA). The BMMCs were collected from the serum-Ficoll interface with a disposable Pasteur pipette and transferred to the 50 ml conical tube with 2% patient serum in 1 × PBS. The BMMCs were counted using a haemacytometer and viability was assessed using Trypan blue. The BMMCs were resuspended in 90% FBS, 10% DMSO freezing media and frozen using a Thermo Scientific Nalgene Mr Frosty (Thermo Scientific) in a -80 °C freezer for 24 h before being transferred to liquid nitrogen for long-term storage."
}
],
"tool": [
{
"@type": "HowToTool",
"name": "Cell capture efficiency calculation"
},
{
"@type": "HowToTool",
"name": "Chimerism assay"
},
{
"@type": "HowToTool",
"name": "Comparison between fresh and frozen PBMCs"
},
{
"@type": "HowToTool",
"name": "Technical demonstration with cell lines and synthetic RNAs"
},
{
"@type": "HowToTool",
"name": "Technical demonstration with cell lines and synthetic RNAs"
},
{
"@type": "HowToTool",
"name": "Technical demonstration with cell lines and synthetic RNAs"
},
{
"@type": "HowToTool",
"name": "Detection of individual populations in mixed samples"
},
{
"@type": "HowToTool",
"name": "Subpopulation discovery from a large immune population"
}
],
"supply": [
{
"@type": "HowToSupply",
"name": "Chimerism assay"
},
{
"@type": "HowToSupply",
"name": "Technical demonstration with cell lines and synthetic RNAs"
},
{
"@type": "HowToSupply",
"name": "Single-cell analysis of transplant bone marrow samples"
},
{
"@type": "HowToSupply",
"name": "Cell lines and transplant patient samples"
},
{
"@type": "HowToSupply",
"name": "Cell lines and transplant patient samples"
},
{
"@type": "HowToSupply",
"name": "Estimation of RNA content per cell"
},
{
"@type": "HowToSupply",
"name": "Cell preparation"
},
{
"@type": "HowToSupply",
"name": "Cell preparation"
}
],
"isBasedOn": {
"@type": "ScholarlyArticle",
"headline": "Massively parallel digital transcriptional profiling of single cells",
"datePublished": "2017",
"author": [
{
"@type": "Person",
"name": "Grace X. Y. Zheng"
},
{
"@type": "Person",
"name": "Jessica M. Terry"
},
{
"@type": "Person",
"name": "Phillip Belgrader"
},
{
"@type": "Person",
"name": "Paul Ryvkin"
},
{
"@type": "Person",
"name": "Zachary W. Bent"
},
{
"@type": "Person",
"name": "Ryan Wilson"
},
{
"@type": "Person",
"name": "Solongo B. Ziraldo"
},
{
"@type": "Person",
"name": "Tobias D. Wheeler"
},
{
"@type": "Person",
"name": "Geoff P. McDermott"
},
{
"@type": "Person",
"name": "Junjie Zhu"
},
{
"@type": "Person",
"name": "Mark T. Gregory"
},
{
"@type": "Person",
"name": "Joe Shuga"
},
{
"@type": "Person",
"name": "Luz Montesclaros"
},
{
"@type": "Person",
"name": "Jason G. Underwood"
},
{
"@type": "Person",
"name": "Donald A. Masquelier"
},
{
"@type": "Person",
"name": "Stefanie Y. Nishimura"
},
{
"@type": "Person",
"name": "Michael Schnall-Levin"
},
{
"@type": "Person",
"name": "Paul W. Wyatt"
},
{
"@type": "Person",
"name": "Christopher M. Hindson"
},
{
"@type": "Person",
"name": "Rajiv Bharadwaj"
},
{
"@type": "Person",
"name": "Alexander Wong"
},
{
"@type": "Person",
"name": "Kevin D. Ness"
},
{
"@type": "Person",
"name": "Lan W. Beppu"
},
{
"@type": "Person",
"name": "H. Joachim Deeg"
},
{
"@type": "Person",
"name": "Christopher McFarland"
},
{
"@type": "Person",
"name": "Keith R. Loeb"
},
{
"@type": "Person",
"name": "William J. Valente"
},
{
"@type": "Person",
"name": "Nolan G. Ericson"
},
{
"@type": "Person",
"name": "Emily A. Stevens"
},
{
"@type": "Person",
"name": "Jerald P. Radich"
},
{
"@type": "Person",
"name": "Tarjei S. Mikkelsen"
},
{
"@type": "Person",
"name": "Benjamin J. Hindson"
},
{
"@type": "Person",
"name": "Jason H. Bielas"
}
],
"identifier": "10.1038/ncomms14049"
}
},
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Experiments",
"item": "https://replicatescience.com/experiments"
},
{
"@type": "ListItem",
"position": 2,
"name": "Massively parallel digital transcriptional profiling of single cells methods",
"item": "https://replicatescience.com/experiments/massively-parallel-digital-transcriptional-profiling-of-single-cells-methods-grace-x-y-zheng-pmc5241818/massively-parallel-digital-transcriptional-profiling-of-single-cells-mlph0qdo"
}
]
}
]