A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level

Mair, Florian; Erickson, Jami R.; Voillet, Valentin; Simoni, Yannick; Bi, Timothy; Tyznik, Aaron J.; Martin, Jody; Gottardo, Raphaël; Newell, Evan W.; Prlic, Martin

doi:10.1016/j.celrep.2020.03.063

Cited by 88 publications

(72 citation statements)

References 45 publications

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“…Analysis of metabolic aspects by single-cell RNA sequencing using with novel analytical approaches could offer additional insights 52 . Especially once challenges related to RNA stability following fixation and permeabilization are resolved, antibody-sequencing hybrid technologies 53 , 54 would present an exciting platform to implement scMEP, combining the unbiased nature of RNA sequencing with the large dynamic range of protein expression and the ability to assess post-transcriptional and post-translational regulation offered by antibody-based technologies 55 . In addition, metabolic profiling could be further extended by combining single-cell analysis of metabolic regulation with determination of epigenetic features 51 .…”

Section: Discussionmentioning

confidence: 99%

Single-cell metabolic profiling of human cytotoxic T cells

et al. 2020

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Cellular metabolism regulates immune cell activation, differentiation and effector functions but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. Here, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using a high-dimensional antibody-based approach. We employed mass cytometry (CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naïve and memory CD8 + T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with imaging mass spectrometry (MIBI-TOF), we uncovered the spatial organization of metabolic programs, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

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Section: Discussionmentioning

confidence: 99%

Single-cell metabolic profiling of human cytotoxic T cells

et al. 2020

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“…By avoiding the usual requirement for processing distinct samples individually, these technologies increase scRNA-seq cell and sample throughput while minimizing technical confounders (e.g., doublets and batch effects). Two main types of sample multiplexing approaches have been described: (i) in silico genotyping using natural [ 7 – 10 ] or artificial [ 11 , 12 ] genomic variants and (ii) tagging cell membranes with sample-specific DNA barcodes using lipid-modified oligonucleotides (LMOs; e.g., MULTI-seq) [ 2 ], DNA-conjugated antibodies [ 3 – 5 ] (e.g., BD single-cell multiplexing kit (SCMK) [ 5 ]), or methyltetrazine-modified DNA “ClickTags” [ 6 ]). Despite the increasing popularity of sample multiplexing, benchmarking studies aiming to measure transcriptional changes induced by mixing cell suspensions during scRNA-seq sample preparation have not been described.…”

Section: Introductionmentioning

confidence: 99%

“…1 ; Experimental methods ). To record the donor-of-origin for each cell, PBMC samples were tagged with donor-specific MULTI-seq [ 2 ] and/or SCMK antibody-DNA [ 5 ] barcodes. PBMCs were mixed for 30 min at 4 °C prior to emulsion across four droplet microfluidics lanes (10x Genomics) at room temperature.…”

Section: Introductionmentioning

confidence: 99%

No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

et al. 2021

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Background Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Results Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets. Conclusions We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

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“…Circuit characteristics are often studied by single-cell measurements using fluorescent markers on few proteins [ 11 ]. These traditional measurements—with the exception of a few emerging technologies that simultaneously monitor transcription and translation [ 12 , 13 ]—cannot visualize many other underlying species that are involved in dictating gene expression dynamics. For example, nucleic acids, protein-complexes, protein-nucleic acid complexes remain invisible.…”

Section: Introductionmentioning

confidence: 99%

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks

Firman

Huihui

Clark

et al. 2021

Entropy

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Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time—recorded at frequent intervals—over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.

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A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level

Cited by 88 publications

References 45 publications

Single-cell metabolic profiling of human cytotoxic T cells

Single-cell metabolic profiling of human cytotoxic T cells

No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks

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