Exploring endocytic compartment morphology with systematic genetics and single cell image analysis

ABSTRACTEndocytosis is a conserved process that mediates the internalization of nutrients and plasma membrane components, including receptors, for sorting to endosomes and the vacuole (lysosome). We combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments: coat proteins, actin patches, late endosome, and vacuole. This unbiased approach identified 17 mutant phenotypes and ∼1600 genes whose perturbation affected at least one of the four compartments. Numerous mutants were associated with multiple phenotypes, indicating that morphological pleiotropy is often seen within the endocytic pathway. Morphological profiles based on the 17 aberrant phenotypes were highly correlated for functionally related genes, enabling prediction of gene function. Incomplete penetrance was prevalent, and single-cell analysis enabled exploration of the mechanisms underlying cellular heterogeneity, which include replicative age, organelle inheritance, and stress response.

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Quantification of single-cell nanoparticle concentrations and the distribution of these concentrations in cell population

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.1152 ◽

2014 ◽

Vol 11 (94) ◽

pp. 20131152 ◽

Cited By ~ 21

Author(s):

Jason T. Rashkow ◽

Sunny C. Patel ◽

Ryan Tappero ◽

Balaji Sitharaman

Keyword(s):

Single Cell ◽

Inductively Coupled Plasma ◽

Single Cell Analysis ◽

Single Cells ◽

Large Population ◽

Cellular Heterogeneity ◽

Physical Parameters ◽

Animal Cells ◽

Promising Tool ◽

Icp Ms

Quantification of nanoparticle uptake into cells is necessary for numerous applications in cellular imaging and therapy. Herein, synchrotron X-ray fluorescence (SXRF) microscopy, a promising tool to quantify elements in plant and animal cells, was employed to quantify and characterize the distribution of titanium dioxide (TiO 2 ) nanosphere uptake in a population of single cells. These results were compared with average nanoparticle concentrations per cell obtained by widely used inductively coupled plasma mass spectrometry (ICP-MS). The results show that nanoparticle concentrations per cell quantified by SXRF were of one to two orders of magnitude greater compared with ICP-MS. The SXRF results also indicate a Gaussian distribution of the nanoparticle concentration per cell. The results suggest that issues relevant to the field of single-cell analysis, the limitation of methods to determine physical parameters from large population averages leading to potentially misleading information and the lack of any information about the cellular heterogeneity are equally relevant for quantification of nanoparticles in cell populations.

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Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

Genome Biology ◽

10.1186/s13059-021-02267-5 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 4

Author(s):

Harrison Specht ◽

Edward Emmott ◽

Aleksandra A. Petelski ◽

R. Gray Huffman ◽

David H. Perlman ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Single Cell Protein ◽

Cellular Heterogeneity ◽

Cell Protein ◽

Alternatively Activated Macrophages ◽

Monocytes And Macrophages ◽

Molecular Phenotypes ◽

Post Transcriptional Regulation

Abstract Background Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. Results To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Conclusions Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

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Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains

10.1101/393769 ◽

2018 ◽

Cited By ~ 7

Author(s):

Min Jung ◽

Daniel Wells ◽

Jannette Rusch ◽

Suhaira Ahmed ◽

Jonathan Marchini ◽

...

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Web Application ◽

Single Cell Analysis ◽

De Novo ◽

Single Cells ◽

Cellular Heterogeneity ◽

Mouse Strains ◽

Seminiferous Tubules ◽

Sequence Motifs

AbstractBy removing the confounding factor of cellular heterogeneity, single cell genomics can revolutionize the study of development and disease, but methods are needed to simplify comparison among individuals. To develop such a framework, we assayed the transcriptome in 62,600 single cells from the testes of wildtype mice, and mice with gonadal defects due to disruption of the genes Mlh3, Hormad1, Cul4a or Cnp. The resulting expression atlas of distinct cell clusters revealed novel markers and new insights into testis gene regulation. By jointly analysing mutant and wildtype cells using a model-based factor analysis method, SDA, we decomposed our data into 46 components that identify novel meiotic gene regulatory programmes, mutant-specific pathological processes, and technical effects. Moreover, we identify, de novo, DNA sequence motifs associated with each component, and show that SDA can be used to impute expression values from single cell data. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/- and Hormad1-/- testes, an area typically associated with immune privilege. We provide a web application to enable interactive exploration of testis gene expression and components at http://www.stats.ox.ac.uk/~wells/testisAtlas.html

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SIMBA: SIngle-cell eMBedding Along with features

10.1101/2021.10.17.464750 ◽

2021 ◽

Author(s):

Huidong Chen ◽

Jayoung Ryu ◽

Michael Edward Vinyard ◽

Adam Lerer ◽

Luca Pinello

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Dna Sequences ◽

Single Cell Analysis ◽

Single Cells ◽

Cellular Heterogeneity ◽

Omics Data ◽

Cell Analysis ◽

Genomic Features ◽

The Individual

Recent advances in single cell omics technologies enable the individual or joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. However, cellular processes are defined in individual cells and inherently involve multiple genomic features that interact with each other and together provide complementary views on principles of gene regulation. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a single-cell embedding method that embeds single cells along with their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for cellular heterogeneity study, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a common way and thus simplifies the development of new analyses and integration of other single-cell modalities.

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A strategy to address dissociation-induced compositional and transcriptional bias for single-cell analysis of the human mammary gland

10.1101/2021.02.11.430721 ◽

2021 ◽

Author(s):

Lisa K. Engelbrecht ◽

Alecia-Jane Twigger ◽

Hilary M. Ganz ◽

Christian J. Gabka ◽

Andreas R. Bausch ◽

...

Keyword(s):

Mammary Gland ◽

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cellular Heterogeneity ◽

Normal Mammary Gland ◽

Isolated Cells ◽

Tissue Dissociation ◽

New Strategy ◽

Gland Function

SummarySingle-cell transcriptomics provide insights into cellular heterogeneity and lineage dynamics that are key to better understanding normal mammary gland function as well as breast cancer initiation and progression. In contrast to murine tissue, human mammary glands require laborious dissociation protocols to isolate single cells. This leads to unavoidable procedure-induced compositional and transcriptional bias. Here, we present a new strategy on how to identify and minimize systematic error by combining different tissue dissociation strategies and then directly comparing composition and transcriptome of isolated cells using single-cell RNA sequencing and flow cytometry. Depending on the tissue isolation strategy, we found dramatic differences in abundance and heterogeneity of certain stromal cells types. Moreover, we identified lineage-specific dissociation-induced gene expression changes that, if left unchecked, could lead to misinterpretation of cellular heterogeneity and, since the basal epithelial population is particularly affected by this, wrongful assignment of putative stem cell populations.

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Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

Communications Biology ◽

10.1038/s42003-020-0896-2 ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Maeve O’Huallachain ◽

Felice-Alessio Bava ◽

Mary Shen ◽

Carolina Dallett ◽

Sri Paladugu ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Single Cell Analysis ◽

Single Cells ◽

Model Systems ◽

Cellular Heterogeneity ◽

Human Model ◽

Targeted Analysis ◽

Technological Advances ◽

And Function

AbstractSingle-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

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Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity

10.1101/665307 ◽

2019 ◽

Cited By ~ 20

Author(s):

Harrison Specht ◽

Edward Emmott ◽

Aleksandra A. Petelski ◽

R. Gray Huffman ◽

David H. Perlman ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Single Cell Protein ◽

Cellular Heterogeneity ◽

Cell Protein ◽

Alternatively Activated Macrophages ◽

Monocytes And Macrophages ◽

Molecular Phenotypes ◽

Post Transcriptional Regulation

AbstractMacrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of limitations of quantitative single-cell protein analysis. To overcome this limitation, we developed SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 3,042 proteins in 1,490 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. This gradient correlates to the inflammatory axis of classically and alternatively activated macrophages. Parallel measurements of transcripts by 10x Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. The joint distributions of proteins and transcripts allowed exploring regulatory interactions, such as between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Our methodology lays the foundation for quantitative single-cell analysis of proteins by mass-spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.Abstract Figure

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A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy

10.1101/745679 ◽

2019 ◽

Cited By ~ 6

Author(s):

Erwin M. Schoof ◽

Nicolas Rapin ◽

Simonas Savickas ◽

Coline Gentil ◽

Eric Lechman ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Mass ◽

Science Research ◽

Cell Types ◽

Cellular Heterogeneity ◽

Significant Shift ◽

Cell Analysis ◽

Computational Pipeline

AbstractIn recent years, cellular life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. Single-cell assays at the protein level have been predominantly limited to targeted, antibody-based methods. However, here we present an experimental and computational pipeline, which establishes a comprehensive single-cell mass spectrometry-based proteomics workflow.By exploiting a leukemia culture system, containing functionally-defined leukemic stem cells, progenitors and terminally differentiated blasts, we demonstrate that our workflow is able to explore the cellular heterogeneity within this aberrant developmental hierarchy. We show our approach is capable to quantifying hundreds of proteins across hundreds of single cells using limited instrument time. Furthermore, we developed a computational pipeline (SCeptre), that effectively clusters the data and permits the extraction of cell-specific proteins and functional pathways. This proof-of-concept work lays the foundation for future global single-cell proteomics studies.

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Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Micromachines ◽

10.3390/mi10050311 ◽

2019 ◽

Vol 10 (5) ◽

pp. 311 ◽

Cited By ~ 2

Author(s):

Iordania Constantinou ◽

Michael Jendrusch ◽

Théo Aspert ◽

Frederik Görlitz ◽

André Schulze ◽

...

Keyword(s):

Image Analysis ◽

Single Cell ◽

Cell Imaging ◽

Single Cell Analysis ◽

Single Cells ◽

Microfluidic Devices ◽

Complex Data ◽

Shot Classification ◽

Single Cell Imaging ◽

Small Channels

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

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SIMBA: SIngle-cell eMBedding Along with features

10.21203/rs.3.rs-1180954/v1 ◽

2022 ◽

Author(s):

Huidong Chen ◽

Jayoung Ryu ◽

Michael Vinyard ◽

Adam Lerer ◽

Luca Pinello

Keyword(s):

Single Cell ◽

Dna Sequences ◽

Single Cell Analysis ◽

Single Cells ◽

Cellular Heterogeneity ◽

Omics Data ◽

Cell Analysis ◽

Biologically Relevant ◽

Latent Space ◽

The Individual

Abstract Recent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities.

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