Protein and RNA Quantification of Multiple Genes in Single Cells

Kays, Ibrahim; Chen, Brian Edwin

doi:10.2144/btn-2018-0130

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…To quantify multiple genes or alleles simultaneously, other split fluorescent proteins (Do and Boxer, 2011;Kamiyama et al, 2016;Kerppola, 2006) can be adapted for PTR for multicolor imaging of protein synthesis. For example, we have previously used CRISPR-Cas9 genome editing to insert PQRs with different fluorescent proteins into multiple endogenous genes (Kays and Chen, 2019;Lo et al, 2015) or to track each parental allele (Lo and Chen, 2019). To this end, we used a split red fluorescent protein, mCherry (Fan et al, 2008), to track protein synthesis of two genes simultaneously (Methods).…”

Section: Resultsmentioning

confidence: 99%

Tracking and Measuring Local Protein Synthesis In Vivo

Kays

2021

Preprint

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Detecting when and how much a protein molecule is synthesized is important for understanding cell function, but current methods have poor cellular or temporal resolution or are destructive to cells. Here, we developed a technique to detect and quantify subcellular protein synthesis events in real time in vivo. This Protein Translation Reporting (PTR) technique uses a genetic tag that produces a stoichiometric ratio of a small peptide portion of a split fluorescent protein and the protein of interest during protein synthesis. We show that the split fluorescent protein peptide can generate fluorescence within milliseconds upon binding the larger portion of the fluorescent protein, and that the fluorescence intensity is directly proportional to the number of molecules of the protein of interest synthesized. Using PTR, we tracked and measured protein synthesis events in single cells over time in vivo. We use split red fluorescent protein to detect multiple genes or alleles in single cells simultaneously. We also split a photoswitchable fluorescent protein to photoconvert the reconstituted fluorescent protein to a different channel and arbitrarily reset the time of detection of synthesis events, continually over time.

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Section: Resultsmentioning

confidence: 99%

Tracking and Measuring Local Protein Synthesis In Vivo

Kays

2021

Preprint

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“…Despite these important developments, it still lacks “multi‐omics” technologies that can effectively link cellular transcriptional states with intracellular, post‐translational states. Recent technological breakthroughs have linked single‐cell transcriptomics data with quantitative protein and metabolites measurements, mainly by qPCR [ 169 , 170 ] and microchip, [ 171 ] as well as by combining FC index sorting and barcoded antibodies with sc‐RNAseq. [ 94 , 118 , 172 , 173 ]…”

Section: Multi‐omics Single‐cell Analysismentioning

confidence: 99%

The Intriguing Landscape of Single‐Cell Protein Analysis

Xie

2022

Advanced Science

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Profiling protein expression at single‐cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single‐cell protein analysis methods are developed rapidly, which enable high‐throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single‐cell multi‐omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single‐cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

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“…To confirm conclusions solely made on the basis of scRNA-seq, it is common practice to validate expression data by scRT-qPCR [26] primarily to control the elevated amount of technical noise and thus dropouts. Several scRT-qPCR workflows have been described [27][28][29][30][31] as well as a few scRT-ddPCR workflows [32][33][34]. The majority of these workflows use fluorescence-activated cell sorting (FACS) for single-cell isolation [28,29,31,32,35], while other studies use microfluidic devices [33,34], micromanipulators [27] or manual cell picking [30].…”

mentioning

confidence: 99%

“…Thus, DE analysis from scRNA-seq must be independently confirmed by single-cell PCR [25]. Several scRT-qPCR workflows have been described [26][27][28][29][30] as well as a few scRT-ddPCR workflows [31][32][33]. The majority of these workflows use fluorescence-activated cell sorting (FACS) for single-cell isolation [27,28,30,31,34], while other studies use microfluidic devices [32,33], micromanipulators [26] or manual cell picking [29].…”

mentioning

confidence: 99%

“…Several scRT-qPCR workflows have been described [26][27][28][29][30] as well as a few scRT-ddPCR workflows [31][32][33]. The majority of these workflows use fluorescence-activated cell sorting (FACS) for single-cell isolation [27,28,30,31,34], while other studies use microfluidic devices [32,33], micromanipulators [26] or manual cell picking [29]. Despite its widespread use, single-cell isolation with FACS requires high sample input and the inherent shear forces can damage the cells and impair RNA integrity [34,35].…”

mentioning

confidence: 99%

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Validation of scRNA-seq by scRT-ddPCR using the example ofErbB2in MCF7 cells

Lange

Groß

Jeney

et al. 2022

Preprint

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Single-cell RNA sequencing (scRNA-seq) can unmask transcriptional heterogeneity facilitating the detection of rare subpopulations at unprecedented resolution. In response to challenges related to coverage and quantity of transcriptome analysis, the lack of unbiased and quantitative validation methods hampers further improvements. Digital PCR (dPCR) represents such a method as the inherent partitioning could enhance detection by increasing the effective concentration of nucleic acids. Thus, we have developed a novel, integrated workflow combining down-scaled, single-cell Smart-seq2 and absolute quantitative, single-cell digital PCR. Our workflow reduces biological and technical variability by analyzing single cells from the same population using identical steps for liquid and single-cell handling. Using two breast cancer cell lines, MCF7 and BT-474, we validated the performance of our down-scaled Smart-seq2 by comparative clustering with published data and our scRT-ddPCR by comparison of absolute gene mRNA counts to bulk methods. We observed significant differences in signal distributions of low-abundant ErbB2 in MCF7 between scRNA-seq and scRT-ddPCR. The differences are mirrored in the calculated fold changes. We assume that low-abundant transcripts suffer from dropouts in scRNA-seq while high abundant transcripts (such as ACTB or ErbB2 in BT-474 cells) suffer from minimal dropouts in the down-scaled Smart-seq2. In scRT-ddPCR however, the inherent partitioning of the reaction volume increases the effective concentration of mRNAs and thus improves sensitivity. As a conclusion, our workflow combines transcriptome-wide gene expression profiling by scRNA-seq with the precise and reliable determination of absolute gene mRNA per cell counts and fold changes by scRT-ddPCR. This can correct biased conclusions made solely on the basis of scRNA-seq. We think this workflow is a valuable addition to the single-cell transcriptomic research toolbox and could even become a new standard in fold change validation because of its reliability, ease of use and increased sensitivity.

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Protein and RNA Quantification of Multiple Genes in Single Cells

Cited by 8 publications

References 23 publications

Tracking and Measuring Local Protein Synthesis In Vivo

Tracking and Measuring Local Protein Synthesis In Vivo

The Intriguing Landscape of Single‐Cell Protein Analysis

Validation of scRNA-seq by scRT-ddPCR using the example ofErbB2in MCF7 cells

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