A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

Abstract

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state, with molecular profiles. This inability to integrate a historical live-cell phenotype, such as invasiveness, cell:cell interactions, and changes in spatial positioning, with multi-omic data, creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomics and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live-cells. We begin with cells stably expressing a photoconvertible fluorescent protein and employ live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulation for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live-cell phenotype and multi-omic heterogeneity within normal and diseased cellular populations.

Fig. 1 |. SaGA schematic to isolate distinct cell(s) based upon live, user-defined phenotypic criteria.

Schematic showing three broad steps of SaGA: 1) Preparation, 2) Selection and Isolation, and 3) Analysis. SaGA can be applied to a variety of cell conditions, such as non-adherent, 3-dimensional (3D), and 2-dimensional (2D), for selection, isolation, and analysis of live subpopulations within a parental population. Cells stably expressing a photoconvertible tag can be precisely photoconverted (from green to red) based upon live, user-defined, phenotypic criteria. These red photoconverted cells are then isolated utilizing fluorescence activated cell sorting (FACS) for multi-omic analysis and/or cell cultivation for long-term in vitro and in vivo analyses. Created with Biorender.com.

Fig. 2 |. SaGA workflow.

Each panel provides an example of a major component of SaGA: preparation, selection and isolation, and analysis. a. 3D spheroid invasion assay set-up beginning with spheroid formation in a low adherence 96-well plate to embedment and invasion in recombinant basement membrane. Scale bar, 250 m. b. Dendra2 visualization under non-adherent, 3D and 2D conditions. 2D conditions are shown utilizing both nuclear- (H2B-Dendra2) and membrane- (Pal-Dendra2) localized protein tags. Scale bar, 50 μm. c. Defining a region of interest (ROI) ( white ) for cell selection and photoconversion. Scale bar, 50 μm. d. Matrix degradation in 3D conditions utilizing collagenase/dispase cocktail. e. FACS plot showing non-photoconverted (−) and photoconverted (+) cells. f. 3D spheroid invasion assay with H1299 parental population and SaGA-isolated leader and follower subpopulations. Scale bar, 250 μm. g. Invasive area and spheroid circularity quantification. *p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test.

Fig. 3 |. Potential loss of heterogeneity and error sources and measures to minimize them.

Cellular loss of heterogeneity can occur during sample preparation, selection and isolation, and analysis. Listed is each major stage of SaGA (preparation, selection and isolation, and analysis) with potential problems (bulleted above within each panel) that can occur and respective potential solutions (bulleted below within each panel). Graphical images created with Biorender.com.

Fig. 4 |. Example photoconversion in different cell culture conditions.

a. Cells stably expressing a photoconvertible tag (ex: H2B-Dendra2, Pal-Dendra2) can be prepared under non-adherent, 3D, or 2D experimental conditions which illicit distinct and imageable cellular response for photoconversion. Non-adherent conditions were performed with RPMI8226 myeloma cells; H1299 lung cancer cells were used for all other conditions. Scale bar, 50 μm. b, c. Integrated density (relative fluorescence units) quantification of 6 or more cells pre- and post-photoconversion in the green (b) and red (c) channels, emission peaks, 507 nm, and 573 nm, respectively. d, e. Quantification of integrated density percent change of 6 or more cells pre- and post- photoconversion in the green (d) and red (e) channels.

Fig. 5 |. Cell selection and isolation optimization.

a. Flow plots illustrating stepwise isolation of live photoconverted cells. 8 % 405 nm laser line intensity utilized in positive control. b. False positive photoconverted cells due to light reflection off the glass plate at varying photoconversion laser intensities at 405 nm. c. Representative merged image showing photoconversion of multiple cells (orange and yellow cells) in 3D, where intensity change is measured in a neighboring, non-photoconverted cell (representative nearby cell, ). Quantification showing fold change of normalized red emission after rounds of photoconversion are complete. d. Representative merged image showing photoconversion in multiple cells (orange and yellow cells) in 2D, where intensity change is measured in a neighboring, non-photoconverted cell (representative nearby cell, ). Quantification showing fold change of normalized red emission after rounds of photoconversion are complete. *p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test. Scale bar, 50 μm.

Fig. 6 |. SaGA-isolated single cell RNA sequencing and analysis.

a. Workflow schematic of collection of SaGA-isolated cells for single cell RNA sequencing and analysis. H1299 cells were used to perform a 3D spheroid invasion assay for a 24 h invasion period. Upon application of SaGA, user-defined cell populations (Leaders and Followers) were sorted directly into a 96-well plate with RNA lysis buffer, frozen at − 80 °C, and subsequently processed for single-cell RNA sequencing. Created with Biorender.com. b. tSNE plot of single H1299 cells (n = 105) based upon expression of the most variably expressed genes (n = 1,155). tSNE clusters were determined by cell positioning within the plot. c. Mutation profile plot for n = 171 single cells using previously identified H1299 leader- and follower- specific mutations. d. tSNE plot with each cell labeled by its mutation profile from panel c. e. tSNE plot with each cell labeled by high (defined as [log2 (normalized counts + 1)] >2 or low <2 expression of Myo10. f. Quantification of Myo10 expression for each tSNE cluster. *p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test.