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The emerging phenomenon of cellular heterogeneity in tissue requires single-cell resolution studies. A specific challenge for suspension-based single-cell analysis is the preservation of intact cell states when single cells are isolated from tissue contexts, in order to enable downstream analyses to extract accurate, native information. We have developed DISSECT (Disaggregation for Intracellular Signaling in Single Epithelial Cells from Tissue) coupled to mass cytometry (CyTOF: Cytometry by Time-of-Flight), an experimental approach for profiling intact signaling states of single cells from epithelial tissue specimens. We have previously applied DISSECT-CyTOF to fresh mouse intestinal samples and to Formalin-Fixed, Paraffin-Embedded (FFPE) human colorectal cancer specimens. Here, we present detailed protocols for each of these procedures, as well as a new method for applying DISSECT to cryopreserved tissue slices. We present example data for using DISSECT on a cryopreserved specimen of the human colon to profile its immune and epithelial composition. These techniques can be used for high-resolution studies for monitoring disease-related alternations in different cellular compartments using specimens stored in cryopreserved or FFPE tissue banks.
Lung epithelial lineages have been difficult to maintain in pure form in vitro, and lineage-specific reporters have proven invaluable for monitoring their emergence from cultured pluripotent stem cells (PSCs). However, reporter constructs for tracking proximal airway lineages generated from PSCs have not been previously available, limiting the characterization of these cells. Here, we engineer mouse and human PSC lines carrying airway secretory lineage reporters that facilitate the tracking, purification, and profiling of this lung subtype. Through bulk and single-cell-based global transcriptomic profiling, we find PSC-derived airway secretory cells are susceptible to phenotypic plasticity exemplified by the tendency to co-express both a proximal airway secretory program as well as an alveolar type 2 cell program, which can be minimized by inhibiting endogenous Wnt signaling. Our results provide global profiles of engineered lung cell fates, a guide for improving their directed differentiation, and a human model of the developing airway.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
It has been widely recognized that individual cells that exist within a large population of cells, even if they are genetically identical, can have divergent molecular makeups resulting from a variety of factors, including local environmental factors and stochastic processes within each cell. Presently, numerous approaches have been described that permit the resolution of these single-cell expression differences for RNA and protein; however, relatively few techniques exist for the study of lipids and metabolites in this manner. This study presents a methodology for the analysis of metabolite and lipid expression at the level of a single cell through the use of imaging mass spectrometry on a high-performance Fourier transform ion cyclotron resonance mass spectrometer. This report provides a detailed description of the overall experimental approach, including sample preparation as well as the data acquisition and analysis strategy for single cells. Applying this approach to the study of cultured RAW264.7 cells, we demonstrate that this method can be used to study the variation in molecular expression with cell populations and is sensitive to alterations in that expression that occurs upon lipopolysaccharide stimulation. Graphical Abstract.
Direct reprogramming of fibroblasts into cardiomyocytes is a promising approach for cardiac regeneration but still faces challenges in efficiently generating mature cardiomyocytes. Systematic optimization of reprogramming protocols requires scalable, objective methods to assess cellular phenotype beyond what is captured by transcriptional signatures alone. To address this question, we automatically segmented reprogrammed cardiomyocytes from immunofluorescence images and analyzed cell morphology. We also introduce a method to quantify sarcomere structure using Haralick texture features, called SarcOmere Texture Analysis (SOTA). We show that induced cardiac-like myocytes (iCLMs) are highly variable in expression of cardiomyocyte markers, producing subtypes that are not typically seen in vivo. Compared to neonatal mouse cardiomyocytes, iCLMs have more variable cell size and shape, have less organized sarcomere structure, and demonstrate reduced sarcomere length. Taken together, these results indicate that traditional methods of assessing cardiomyocyte reprogramming by quantifying induction of cardiomyocyte marker proteins may not be sufficient to predict functionality. The automated image analysis methods described in this study may enable more systematic approaches for improving reprogramming techniques above and beyond existing algorithms that rely heavily on transcriptome profiling.
Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell-state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories.
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
Spatiotemporal balancing of cellular proliferation and differentiation is crucial for postnatal tissue homoeostasis and organogenesis. During embryonic development, pancreatic progenitors simultaneously proliferate and differentiate into the endocrine, ductal and acinar lineages. Using in vivo clonal analysis in the founder population of the pancreas here we reveal highly heterogeneous contribution of single progenitors to organ formation. While some progenitors are bona fide multipotent and contribute progeny to all major pancreatic cell lineages, we also identify numerous unipotent endocrine and ducto-endocrine bipotent clones. Single-cell transcriptional profiling at E9.5 reveals that endocrine-committed cells are molecularly distinct, whereas multipotent and bipotent progenitors do not exhibit different expression profiles. Clone size and composition support a probabilistic model of cell fate allocation and in silico simulations predict a transient wave of acinar differentiation around E11.5, while endocrine differentiation is proportionally decreased. Increased proliferative capacity of outer progenitors is further proposed to impact clonal expansion.
The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell-cycle status of tumor cells. Heterogeneity in tumor cell-cycle status (e. g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell-cycle status is closely linked to cellular metabolism. Thus, this study applies cell-level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two-photon microscopy and time-correlated single photon counting are used to measure optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), NAD(P)H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P)H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares - discriminant analysis (PLS-DA) are used to exploit all measurements together. Leave-one-out cross validation of the model yielded high classification accuracies (92.4 and 90.1 % for two and three populations, respectively). OMI and PLS-DA also identifies each sub-population within heterogeneous samples. These results establish single-cell analysis with OMI and PLS-DA as a label-free method to distinguish cell-cycle status within intact samples. This approach could be used to incorporate cell-level tumor heterogeneity in cancer drug development.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Mass cytometry is a single-cell biology technique that samples >500 cells per second, measures >35 features per cell, and is sensitive across a dynamic range of >10 relative intensity units per feature. This combination of technical assets has powered a series of recent cytomic studies where investigators used mass cytometry to measure protein and phospho-protein expression in millions of cells, characterize rare cell types in healthy and diseased tissues, and reveal novel, unexpected cells. However, these advances largely occurred in studies of blood, lymphoid tissues, and bone marrow, since the cells in these tissues are readily obtained in single-cell suspensions. This unit establishes a primer for single-cell analysis of solid tumors and tissues, and has been tested with mass cytometry. The cells obtained from these protocols can be fixed for study, cryopreserved for long-term storage, or perturbed ex vivo to dissect responses to stimuli and inhibitors. © 2017 by John Wiley & Sons, Inc.
Copyright © 2017 John Wiley & Sons, Inc.
Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.