A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
October 25th, 2018
Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.
This method can answer key questions in the stem cell and cancer fields such as what stem cell heterogeneity looks like and how therapy in sensitive cancer cells can be isolated. The main advantage of this study is that it combines technically robust single-cell gene expression analysis together with vaccinosis for further downstream characterization of target subpopulations. Though this method can be used to investigate the cancer and the stem cell heterogeneity, it can also give insights into gene priming and lineage potential.
To begin the protocol, on an RNA/DNA-free bench, prepare enough lysis buffer for 96 wells with 10%extra by mixing 390 microliters nuclease free water, 17 microliters of 10%NP-40, 2.8 microliters 10 millimolar dNTP, 10 microliters 0.1 molar DTT, and 5.3 microliters RNAse inhibitor. Vortex and spin down the tube. Next, distribute four microliters of lysis buffer to each well of a 96-well PCR plate and seal the plates with adhesive film.
Spin down the plates to collect the liquid at the bottom. Keep the plates on ice until cell sorting for a maximum of 24 hours. Once the FACS machine has been properly setup, perform reanalysis of the target population by sorting at least 100 target cells into a new microcentrifuge tube with 100 microliters of stain buffer.
FACS analyze the sorted cells by recording the sorted sample and make sure that they end up in the sort gate. Then set up single-cell plate sorting by centering the drop in well A1 in the 96-well plate. When it is centered, sort 50 to 106 micrometer particles in all the wells around the edge of an empty 96-well plate to ensure that all wells will get a cell in the center of each well.
Remove the adhesive film from the plates. Sort a single cell of Lin-CD34 plus CD38 minus cells into 92 out of the 96 wells. Activate index sorting in the FACS sorting software to save the immunophenotypic profile for CD45RA, CD49F and CD90 for each single cell.
Sort two wells with 10 and 20 cells respectively for linearity controls in the PCR amplification. Wells H1 and H2 are usually used. Keep two wells without any cells as no template controls, usually wells H3 and H4.Seal the plates with clear adhesive film and spin the plates at 300 times gravity for one minute.
Snap freeze the plates on dry ice. Then store them at minus 80 degrees Celsius. Make reverse transcription and specific target amplification mix by adding 632.5 microliters of two times reaction mix, 101.2 microliters Taq/SuperscriptIII, 151.8 microliters primer mix, and 0.7 microliters spiked in control RNA.
Vortex the solution and spin it down to collect the liquid at the bottom of the tube. Keep the mix on ice until it is ready to be added to the sample. Next, thaw the lysate plates on ice.
Add 8.75 microliters of the previously prepared reverse transcription and specific target amplification mix to 92 wells, including the linearity and no template controls. Add 8.75 microliters of no reverse transcription control mix to the four remaining wells. Then seal the plates with clear adhesive film and spin the plates to collect liquid at the bottom.
Perform reverse transcription and specific target amplification by running the plate in a PCR machine according to the preamp program. Prepare the assay loading plate by pipetting three microliters of assay loading reagent to each well of a 96-well plate. Add three microliters of each primer to individual wells in the assay loading plate.
Seal the plate with adhesive film and spin it down. After spinning the plate, prepare the dilution plate by pipetting eight microliters of nuclease free water into all the wells of a 96-well plate. Add two microliters of amplified sample to the dilution plate.
Seal the plate with adhesive film. Mix by vortexing the plate for 10 seconds. Then spin down the plate.
Next, prepare the sample loading mix by carefully mixing 352 microliters of master mix with 35.2 microliters of sample loading reagent. Prepare the sample loading plate by aliquoting 3.3 microliters of loading mix to each well of a 96-well plate. Add 2.7 microliters from the diluted sample into each well of the sample loading plate.
Seal the plate with adhesive film and spin it down. Take out a new 96 by 96 microfluidic chip. Prepare inlets by poking them with a syringe with a cap on to make sure that they can be moved.
Add the full volume of the syringes to each valve while tilting the chip to 45 degrees and pressing down the valve. Prime the chip with the IFC controller. Load each assay inlet with 4.25 microliters from each of the wells in the assay loading plate and avoid bubbles.
If bubbles appear in the well, remove them with a pipette tip. Continue loading each sample inlet with 4.25 microliters from each of the wells in the sample loading plate, avoiding bubbles, and if bubbles appear removing them with a pipette tip. Load the chip with the Integrated Fluidics Circuit controller or IFC controller.
Check that the chip looks even and that all chambers have been loaded. Remove dust from the chip's surface by touching it with tape. Finally, run the chip in the multiplex microfluidic gene expression platform.
A heat map of a successful run displays the evenly spread expression across samples with a strong signal of around seven to 25 CTs. This amplification curve of spiked in control RNA verifies that all wells have clear expression of around 10 CT with little to no variation, validating that the pre-amplification and qPCR reactions had been successful for all cells. Principle Component Analysis or PCA which visualizes the similarities among cell groups using dimension reduction can be useful to distinguish clusters of molecularly distinct cells from each other here identifying four subgroups.
To identify the immunophenotype of cells from different molecular clusters, load the index FCS file into FlowJo. Open the script editor and paste the index script, press Run. Each cell will now be in a separate gate.
Add each cell into the layout editor and color them according to the molecularly-defined grouping from SCXV available in the file sample_complete_data.xls. FACS plots show the cell surface marker expression for the hematopoietic stem cell markers CD90 and CD45RA which can be used to discriminate between the clusters and isolate them for functional analysis. Following this procedure, other technique like RNA sequencing of newly discovered subpopulations or in vitro and in vivo experiments can be performed to answer additional question like what molecular mechanism causes this heterogeneity and how does the subpopulation differ functionally.
This strategy paved the way for researchers to not only investigate heterogeneity in normal and malignant hematopoiesis, but can also be used to prospectively isolate the discovered subpopulations to further study them.
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Overview
This article presents a protocol that combines single-cell gene expression analysis with fluorescence-activated cell sorting (FACS) to explore heterogeneity in cell populations. It aims to characterize molecularly distinct cell types, particularly in the context of stem cells and cancer.
Key Study Components
Area of Science
- Cell Biology
- Neuroscience
- Cancer Research
Background
- Bulk gene expression measurements can obscure individual cell differences.
- Understanding stem cell heterogeneity is crucial for therapeutic advancements.
- FACS allows for the isolation of specific cell populations for detailed analysis.
- This method provides insights into gene priming and lineage potential.
Purpose of Study
- To delineate heterogeneity in cell populations.
- To immunophenotypically characterize distinct cell types.
- To investigate the effects of therapies on sensitive cancer cells.
Methods Used
- Preparation of lysis buffer for single-cell analysis.
- Use of fluorescence-activated cell sorting (FACS).
- Single-cell gene expression analysis.
- Downstream characterization of target subpopulations.
Main Results
- The method effectively reveals stem cell heterogeneity.
- It allows for the isolation of specific cancer cell populations.
- Insights into gene priming and lineage potential were obtained.
- The protocol is technically robust and reproducible.
Conclusions
- This approach enhances the understanding of cellular diversity.
- It has potential applications in both stem cell and cancer research.
- The combination of techniques provides a comprehensive analysis of cell populations.
Frequently Asked Questions
Executive Industry Relevance
This combinatorial single-cell approach addresses a critical bottleneck in stem cell and cancer research by preserving single-cell resolution during molecular profiling, enabling precise identification of rare subpopulations that drive therapeutic resistance or lineage differentiation. By linking immunophenotypic markers to gene expression profiles at single-cell resolution, the method enhances target validation confidence and supports mechanistic de-risking in early discovery pipelines. The approach provides a scalable, reproducible workflow for generating quantitative, multimodal data that informs go/no-go decisions in target prioritization and lead identification efforts.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by resolving molecular and immunophenotypic heterogeneity in stem and progenitor populations.
- Operational Value: Supports functional target validation through correlation of surface marker expression with pre-selected gene panels, reducing false positives in target selection.
- Predictive Value: Facilitates identification of molecularly distinct subpopulations for downstream isolation, improving confidence in lineage potential and gene priming assessments.
Screening & Assay Development
- Assay Readiness: Generates standardized, quantitative gene expression data linked to FACS index sorting, enabling reproducible screening of molecular states across cell populations.
- Scalability: Compatible with 96-well formats and microfluidic platforms, supporting medium-throughput screening of pre-defined gene panels in primary human samples.
- Reproducibility: Minimizes technical variability through single-cell lysis in buffer and pre-amplification steps, ensuring consistent detection of low-abundance transcripts.
Translational & Preclinical Research
- Translational Continuity: Enables prospective isolation of molecularly defined subpopulations for functional validation in vitro and in vivo, bridging discovery to preclinical development.
- Biomarker Alignment: Supports identification of surface marker signatures correlated with molecular clusters, aiding in the development of translational biomarkers for patient stratification.
- Risk-Adjusted Advancement: Provides mechanistic insights into heterogeneity that inform preclinical model selection and reduce late-stage biological risk in therapeutic development.
Pipeline & Workflow Integration
The method integrates into early discovery workflows by enabling high-resolution profiling of heterogeneous populations prior to lead identification, particularly in stem cell-driven cancers and regenerative medicine programs. It positions molecular and phenotypic characterization as a continuous, reusable capability rather than a one-off assay, supporting iterative target refinement.
- Discovery Biology: Supports hypothesis testing and pathway clarification by linking single-cell gene expression to immunophenotypic profiles, enabling biological de-risking of targets in hematopoietic and oncogenic contexts.
- Screening: Delivers assay-ready, quantitative outputs from pre-amplified cDNA, facilitating reliable comparison of molecular states across sorted single cells.
- Analytics: Enables principal component analysis (PCA) and heatmap visualization to identify molecular clusters and correlate them with surface marker expression for subpopulation discrimination.
- Translational Research: Connects molecular clustering to functional validation through prospective isolation of subpopulations for downstream RNA sequencing or in vivo assays.
- Enterprise Reuse: Establishes a standardized protocol for multiplexed single-cell analysis that can be adapted across model systems and therapeutic areas, reducing redundant method development.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in target validation by resolving molecular heterogeneity masked in bulk analyses.
- Operational Value: Ensures reproducibility through standardized lysis, pre-amplification, and qPCR workflows compatible with existing FACS and microfluidic infrastructure.
- Strategic Value: Improves capital efficiency by enabling early identification of functionally relevant subpopulations, reducing investment in non-viable targets.
- Portfolio Impact: Supports risk-adjusted prioritization by providing mechanistic rationale for advancing molecularly defined populations into preclinical validation.
Implementation Considerations
- Requires expertise in FACS index sorting, single-cell handling, and microfluidic qPCR platforms.
- Dependent on access to fluorescence-activated cell sorters with index sorting capability and microfluidic gene expression systems.
- Necessitates standardization across teams for lysis buffer preparation, sorting gating strategies, and data analysis pipelines.
- Adaptation to non-hematopoietic systems may require optimization of surface marker panels and lysis conditions.
- Practical limitations include throughput constraints tied to sorting speed and microfluidic chip loading, best suited for focused panels rather than whole-transcriptome screening.
