Single Amino Acid Variant Discovery in Small Numbers of Cells

The miR-183/96/182 cluster regulates sensory innervation, resident myeloid cells and functions of the cornea through cell type-specific target genes

The conserved miR-183/96/182 cluster (miR-183C) is expressed in both corneal resident myeloid cells (CRMCs) and sensory nerves (CSN) and modulates corneal immune/inflammatory responses. To uncover cell type-specific roles of miR-183C in CRMC and CSN and their contributions to corneal physiology, myeloid-specific miR-183C conditional knockout (MS-CKO), and sensory nerve-specific CKO (SNS-CKO) mice were produced and characterized in comparison to the conventional miR-183C KO. Immunofluorescence and confocal microscopy of flatmount corneas, corneal sensitivity, and tear volume assays were performed in young adult naïve mice; 3' RNA sequencing (Seq) and proteomics in the trigeminal ganglion (TG), cornea and CRMCs. Our results showed that, similar to conventional KO mice, the numbers of CRMCs were increased in both MS-CKO and SNS-CKO vs age- and sex-matched WT control littermates, suggesting intrinsic and extrinsic regulations of miR-183C on CRMCs. The number of CRMCs was increased in male vs female MS-CKO mice, suggesting sex-dependent regulation of miR-183C on CRMCs. In the miR-183C KO and SNS-CKO, but not the MS-CKO mice, CSN density was decreased in the epithelial layer of the cornea, but not the stromal layer. Functionally, corneal sensitivity and basal tear volume were reduced in the KO and SNS-CKO, but not the MS-CKO mice. Tear volume in males is consistently higher than female WT mice. Bioinformatic analyses of the transcriptomes revealed a series of cell-type specific target genes of miR-183C in TG sensory neurons and CRMCs. Our data elucidate that miR-183C imposes intrinsic and extrinsic regulation on the establishment and function of CSN and CRMCs by cell-specific target genes. miR-183C modulates corneal sensitivity and tear production through its regulation of corneal sensory innervation.

A review of the current state of single-cell proteomics and future perspective

Single-cell methodologies and technologies have started a revolution in biology which until recently has primarily been limited to deep sequencing and imaging modalities. With the advent and subsequent torrid development of single-cell proteomics over the last 5 years, despite the fact that proteins cannot be amplified like transcripts, it has now become abundantly clear that it is a worthy complement to single-cell transcriptomics. In this review, we engage in an assessment of the current state of the art of single-cell proteomics including workflow, sample preparation techniques, instrumentation, and biological applications. We investigate the challenges associated with working with very small sample volumes and the acute need for robust statistical methods for data interpretation. We delve into what we believe is a promising future for biological research at single-cell resolution and highlight some of the exciting discoveries that already have been made using single-cell proteomics, including the identification of rare cell types, characterization of cellular heterogeneity, and investigation of signaling pathways and disease mechanisms. Finally, we acknowledge that there are a number of outstanding and pressing problems that the scientific community vested in advancing this technology needs to resolve. Of prime importance is the need to set standards so that this technology becomes widely accessible allowing novel discoveries to be easily verifiable. We conclude with a plea to solve these problems rapidly so that single-cell proteomics can be part of a robust, high-throughput, and scalable single-cell multi-omics platform that can be ubiquitously applied to elucidating deep biological insights into the diagnosis and treatment of all diseases that afflict us.

Mass spectrometry-based targeted proteomics for analysis of protein mutations

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

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

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.

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

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.

Defining the carrier proteome limit for single-cell proteomics

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.

An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labeling in a Nanodroplet Sample Preparation Platform

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2 300 proteins identified. Principal component analysis grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. Our platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

Boosting to Amplify Signal with Isobaric Labeling (BASIL) Strategy for Comprehensive Quantitative Phosphoproteomic Characterization of Small Populations of Cells

Comprehensive phosphoproteomic analysis of small populations of cells remains a daunting task due primarily to the insufficient MS signal intensity from low concentrations of enriched phosphopeptides. Isobaric labeling has a unique multiplexing feature where the "total" peptide signal from all channels (or samples) triggers MS/MS fragmentation for peptide identification, while the reporter ions provide quantitative information. In light of this feature, we tested the concept of using a "boosting" sample (e.g., a biological sample mimicking the study samples but available in a much larger quantity) in multiplexed analysis to enable sensitive and comprehensive quantitative phosphoproteomic measurements with <100 000 cells. This simple boosting to amplify signal with isobaric labeling (BASIL) strategy increased the overall number of quantifiable phosphorylation sites more than 4-fold. Good reproducibility in quantification was demonstrated with a median CV of 15.3% and Pearson correlation coefficient of 0.95 from biological replicates. A proof-of-concept experiment demonstrated the ability of BASIL to distinguish acute myeloid leukemia cells based on the phosphoproteome data. Moreover, in a pilot application, this strategy enabled quantitative analysis of over 20 000 phosphorylation sites from human pancreatic islets treated with interleukin-1β and interferon-γ. Together, this signal boosting strategy provides an attractive solution for comprehensive and quantitative phosphoproteome profiling of relatively small populations of cells where traditional phosphoproteomic workflows lack sufficient sensitivity.