Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping

Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform single-cell mechanical testing with high throughput. However, prior work has performed only limited characterization of these platforms' technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, which is a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform's measurements of single-cell mechanical properties, and we demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different labs with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.

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Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping

PLoS ONE ◽

10.1371/journal.pone.0258982 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0258982

Author(s):

Brian Li ◽

Kristen L. Cotner ◽

Nathaniel K. Liu ◽

Stefan Hinz ◽

Mark A. LaBarge ◽

...

Keyword(s):

Mechanical Properties ◽

Single Cell ◽

Mechanical Testing ◽

Cell Types ◽

Biologically Relevant ◽

Analytical Instruments ◽

Technical Variability ◽

Novice Users ◽

High Repeatability

Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform high-throughput single-cell mechanical testing. However, prior work has performed only limited characterization of these platforms’ technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform’s measurements of single-cell mechanical properties. We demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different laboratories with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.

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Selecting single cell clustering parameter values using subsampling-based robustness metrics

BMC Bioinformatics ◽

10.1186/s12859-021-03957-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ryan B. Patterson-Cross ◽

Ariel J. Levine ◽

Vilas Menon

Keyword(s):

Single Cell ◽

Optimal Parameter ◽

Clustering Algorithms ◽

Cell Types ◽

Parameter Selection ◽

Data Set ◽

Biologically Relevant ◽

Cell Clustering ◽

Parameter Values ◽

Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽

10.1186/s12864-020-07358-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tracy M. Yamawaki ◽

Daniel R. Lu ◽

Daniel C. Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Data Interpretation ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

10.1101/2021.08.03.454066 ◽

2021 ◽

Author(s):

Anthony Z Wang ◽

Jay Bowman-Kirigin ◽

Rupen Desai ◽

Pujan Patel ◽

Bhuvic Patel ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Immune Surveillance ◽

Copy Number Variant ◽

Cell Types ◽

Cell Receptor ◽

Immune Microenvironment ◽

Clinical Models ◽

Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

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Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

10.1101/354944 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xuran Wang ◽

Jihwan Park ◽

Katalin Susztak ◽

Nancy R. Zhang ◽

Mingyao Li

Keyword(s):

Single Cell ◽

Cell Types ◽

Cellular Heterogeneity ◽

Tissue Cell ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific ◽

Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

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Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009305 ◽

2021 ◽

Vol 17 (9) ◽

pp. e1009305

Author(s):

Suraj Kannan ◽

Michael Farid ◽

Brian L. Lin ◽

Matthew Miyamoto ◽

Chulan Kwon

Keyword(s):

Stem Cell ◽

Single Cell ◽

Pancreatic Beta Cells ◽

Ad Hoc ◽

Cell Types ◽

Developmental Trajectory ◽

Batch Effects ◽

Biologically Relevant ◽

Novel Approach

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.

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Systematic Comparison of High-Throughput Single-Cell RNAseq Methods for Immune Cell Profiling

10.21203/rs.3.rs-72851/v1 ◽

2020 ◽

Author(s):

Chi-Ming Kevin Li ◽

Tracy M Yamawaki ◽

Daniel R Lu ◽

Daniel C Ellwanger ◽

Dev Bhatt ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the fieldof immunology by deepening the characterization of immune heterogeneity and leading to thediscovery of new subtypes. However, single-cell methods inherently suffer from limitations in therecovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropoutevents. This issue is often compounded by limited sample availability and limited prior knowledge ofheterogeneity, which can confound data interpretation.Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. Weprepared 21 libraries under identical conditions of a defined mixture of two human and two murinelymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluatemethods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expressionsignatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events whichfacilitates the identification of differentially-expressed genes and improves the concordance of singlecellprofiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which canguide selection of a high-throughput single-cell RNA-seq method for profiling more complex immunecellheterogeneity usually found in vivo.

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Single cell micro-pillar-based characterization of endothelial and fibroblast cell mechanics

10.1101/2021.10.11.463878 ◽

2021 ◽

Author(s):

Julia Eckert ◽

Yasmine Abouleila ◽

Thomas Schmidt ◽

Alireza Mashaghi

Keyword(s):

Single Cell ◽

Force Measurement ◽

Traction Force ◽

Fibroblast Cell ◽

Cell Types ◽

Fibroblast Cells ◽

Cellular Processes ◽

Sense And Respond ◽

Underlying Mechanisms

Mechanotransduction, the ability of cells to sense and respond to the mechanical cues from their microenvironment, plays an important role in numerous cellular processes, ranging from cell migration to differentiation. Several techniques have been developed to investigate the underlying mechanisms of mechanotransduction, in particular, force measurement-based techniques. However, we still lack basic single cell quantitative comparison on the mechanical properties of commonly used cell types, such as endothelial and fibroblast cells. Such information is critical to provide a precedent for studying complex tissues and organs that consist of various cell types. In this short communication, we report on the mechanical characterization of the commonly used endothelial and fibroblast cells at the single cell level. Using a micropillar-based assay, we measured the traction force profiles of these cells. Our study showcases differences between the two cell types in their traction force distribution and morphology. The results reported can be used as a reference and to lay the groundwork for future analysis of numerous disease models involving these cells.

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An acoustic platform for single-cell, high-throughput measurements of the viscoelastic properties of cells

10.1101/2020.09.07.286898 ◽

2020 ◽

Author(s):

Valentin Romanov ◽

Giulia Silvani ◽

Huiyu Zhu ◽

Charles D Cox ◽

Boris Martinac

Keyword(s):

Mechanical Properties ◽

Single Cell ◽

High Throughput ◽

Single Cells ◽

Adherent Cells ◽

Dependent Manner ◽

Cellular Processes ◽

Membrane Cytoskeleton ◽

Change Temperature

ABSTRACTCellular processes including adhesion, migration and differentiation are governed by the distinct mechanical properties of each cell. Importantly, the mechanical properties of individual cells can vary depending on local physical and biochemical cues in a time-dependent manner resulting in significant inter-cell heterogeneity. While several different methods have been developed to interrogate the mechanical properties of single cells, throughput to capture this heterogeneity remains an issue. While new high-throughput techniques are slowly emerging, they are primarily aimed at characterizing cells in suspension, whereas high-throughput measurements of adherent cells have proven to be more challenging. Here, we demonstrate single-cell, high-throughput characterization of adherent cells using acoustic force spectroscopy. We demonstrate that cells undergo marked changes in viscoelasticity as a function of temperature, the measurements of which are facilitated by a closed microfluidic culturing environment that can rapidly change temperature between 21 °C and 37 °C. In addition, we show quantitative differences in cells exposed to different pharmacological treatments specifically targeting the membrane-cytoskeleton interface. Further, we utilize the high-throughput format of the AFS to rapidly probe, in excess of 1000 cells, three different cell-lines expressing different levels of a mechanosensitive protein, Piezo1, demonstrating the ability to differentiate between cells based on protein expression levels.

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IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

GigaScience ◽

10.1093/gigascience/giz121 ◽

2019 ◽

Vol 8 (10) ◽

Cited By ~ 2

Author(s):

Yun-Ching Chen ◽

Abhilash Suresh ◽

Chingiz Underbayev ◽

Clare Sun ◽

Komudi Singh ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Sequencing Analysis ◽

Sequencing Data ◽

Peripheral Blood Mononuclear ◽

Biologically Relevant ◽

Single Cell Rna Sequencing ◽

Cell Groups ◽

Cell Ontology

AbstractBackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

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