Characterizing cell interactions at scale with made-to-order droplet ensembles (MODEs)
Ontology highlight
ABSTRACT: Cell-cell interactions are important to numerous biological systems, including tissue microenvironments, the immune system, and cancer. However, current methods for studying cell combinations and interactions are limited in scalability, allowing just hundreds to thousands of multi-cell assays per experiment; this limited throughput makes it difficult to characterize interactions at biologically relevant scales. Here, we describe a new paradigm in cell interaction profiling that allows accurate grouping of cells and characterization of their interactions for tens to hundreds of thousands of combinations. Our approach leverages high throughput droplet microfluidics to construct multicellular combinations in a deterministic process that allows inclusion of programmed reagent mixtures and beads. The combination droplets are compatible with common manipulation and measurement techniques, including imaging, barcode-based genomics, and sorting. We demonstrate the approach by using it to enrich for CAR-T cells that activate upon incubation with target cells, a bottleneck in the therapeutic T cell engineering pipeline. The speed and control of our approach should enable valuable cell interaction studies.
Project description:To fully understand the effect of microspheres on genes coding for toxic products, signalling molecules, cell stress responses and so forth. Gene expression profiling using a microarray-based technology allows the expression analysis of thousands of genes simultaneously. This method is more informative than traditional studies on single genes, providing information on networks of gene expression changes in a high-throughput manner. In particular, this approach has been used successfully to study the interaction between cells and their polymers supports yielding enormous information concerning the cell-polymer interactions. Thus, this technique offers a significant contribution towards fully understanding the nature of beadfected cells and the possible transcriptomic alterations that may occur.
Project description:The combination of single-cell RNA sequencing with CRISPR inhibition/activation provides a high-throughput approach to simultaneously study the effects of hundreds if not thousands of gene perturbations in a single experiment. One recent development in CRISPR-based single-cell techniques introduces a feature barcoding technology which allows for the simultaneous capture of mRNA and gRNA from the same cell. This is achieved by introducing a capture sequence, whose complement can be incorporated into each gRNA, and which can be used to amplify these features prior to sequencing. However, with the technology in its infancy, there is little information available on how such experimental parameters can be optimised. To overcome this, we varied the capture sequence, capture sequence position and gRNA backbone to identify an optimal gRNA scaffold for CRISPR-activation gene perturbation studies. We provide a report on our screening approach along with our observations and recommendations for future use.
Project description:Knowledge of the expression profile and spatial landscape of the transcriptome in individual cells is essential for understanding the rich repertoire of cellular behaviors. Here we report multiplexed error-robust fluorescence in situ hybridization (MERFISH), a single-molecule imaging approach that allows the copy numbers and spatial localizations of thousands of RNA species to be determined in single cells. Using error-robust encoding schemes to combat single-molecule labeling and detection errors, we demonstrated the imaging of 100 – 1000 unique RNA species in hundreds of individual cells. Correlation analysis of the ~10^4 – 10^6 pairs of genes allowed us to constrain gene regulatory networks, predict novel functions for many unannotated genes, and identify distinct spatial distribution patterns of RNAs that correlate with properties of the encoded proteins. A single sample is analyzed
Project description:Single-cell CRISPR screens allow for the exploration of mammalian gene function and genetic regulatory networks, but their utility has been limited in part by their reliance on indirect sgRNA indexing. Here, we present direct capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct capture Perturb-seq enables the detection of multiple distinct sgRNAs expressed from a single vector within individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries. We demonstrate that this approach allows high-throughput investigations of genetic interactions, and we leverage this ability to dissect epistatic interactions between cholesterol biogenesis and DNA repair. We also show that targeting individual genes with multiple sgRNAs per cell improves the efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for single-cell screens. Lastly, we show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.
Project description:The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labelled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.
Project description:Large-scale epigenomic projects have mapped hundreds of thousands of potential regulatory sites in the human genome, but only a small proportion of these elements are proximal to transcription start sites. It is believed that the majority of these sequences are remote promoter-activating genomic sites scattered within several hundreds of kilobases from their cognate promoters and referred to as enhancers. It is still unclear what principles, aside from relative closeness in the linear genome, determine which promoter(s) is controlled by a given enhancer; however, this understanding is of great fundamental and clinical relevance. In recent years, C-methods have become a powerful tool for the identification of enhancer-promoter spatial contacts that, in most cases, reflect their functional link. Here, we present data on spatial interactions of 867 promoters in HeLa cells. This data is obtained with a new hybridization-based promoter Capture-C protocol that makes use of biotinylated dsDNA probes and allows high-resolution promoter interactome description.
Project description:Current expression profiling methods use RNA from hundreds of thousands or thousands cells. Many fields of biology can not use microarrays due to the nature of the biological systems used that are formed by hundreds or dozens of cells. Here we present a method that can handle RNA amount limitation and gives gene expression profiles from as little as 10 cells. We first validate the method hybridizing amplified RNA from MAQC samples A and B. To do that, 25 ng or 100 pg were used and expression profiles obtained as good as when compared to Affymetrix's chemistry for amplification and labeling. The same experiment was done but using sorted cells from two comercial cell lines (SW620 and SW480) obtaining the same differential expression profiling from 2000 cells or 10 cells. The central step of the method is Whole Transcriptome Amplification (WTA) from Sigma that allows the amplification of very small amounts of RNA as starting material.
Project description:Current expression profiling methods use RNA from hundreds of thousands or thousands cells. Many fields of biology can not use microarrays due to the nature of the biological systems used that are formed by hundreds or dozens of cells. Here we present a method that can handle RNA amount limitation and gives gene expression profiles from as little as 10 cells. We first validate the method hybridizing amplified RNA from MAQC samples A and B. To do that, 25 ng or 100 pg were used and expression profiles obtained as good as when compared to Affymetrix's chemistry for amplification and labeling. The same experiment was done but using sorted cells from two comercial cell lines (SW620 and SW480) obtaining the same differential expression profiling from 2000 cells or 10 cells. The central step of the method is Whole Transcriptome Amplification (WTA) from Sigma that allows the amplification of very small amounts of RNA as starting material. MAQC samples A and B where amplified using Whole Transcriptome Amplification (WTA) kit from Sigma starting from 25 ng (standard conditions) or 100 pg in triplicates. Amplified cDNA was the fragmented and labeled using the 10kv2 SNP chemistry from Affymetrix and hybridized onto Affymetrix GeneST arrays. The same method was used for RNA isolated from 2000 and 10 cells from cell lines SW620 and SW480. RNA was isolated by magnetic bead purification after treating sorted cells with high DTT concentration and 65ºC to inactivate RNAses.
Project description:Cell–cell interactions (CCIs) are essential for tissue functionality and targeted therapies, particularly in the context of chimeric antigen receptor T (CAR T) cells. Here, we introduce TyP-HIM-seq, a barcoding approach based on tyrosinase-catalyzed proximity (TyP) labeling. This method enables the simultaneous high-throughput measurement of interacting cells and their mRNA expressions through single-cell sequencing. By translating intercellular contact into in situ chemical labeling of a DNA barcode, TyP-HIM-seq allows for a comprehensive assessment of CCIs and full deconvolution of related molecular pathways. We used TyP-HIM-seq to investigate the CCIs between CD19 CAR-Jurkat cells and Ramos tumor cells.