Simultaneous Measurement of Surface Proteins and Gene Expression from Single Cells

2020 ◽  
pp. 35-46
Author(s):  
Jiadi Luo ◽  
Carla A. Erb ◽  
Kong Chen
Keyword(s):  
Gene Expression ◽  
Simultaneous Measurement ◽  
Single Cells ◽  
Surface Proteins

scholarly journals Simultaneous measurement of biochemical phenotypes and gene expression in single cells

2019 ◽  
Author(s):  
Amanda L. Richer ◽  
Kent A. Riemondy ◽  
Lakotah Hardie ◽  
Jay R. Hesselberth
Keyword(s):  
Gene Expression ◽  
Dna Repair ◽  
Simultaneous Measurement ◽  
General Framework ◽  
Single Cells ◽  
Human Lymphocytes ◽  
Mrna Abundance ◽  
Cell Type ◽  
Functional Heterogeneity ◽  
Cell Type Specific

ABSTRACTMethods to measure heterogeneity among cells are rapidly transforming our understanding of biology but are currently limited to molecular abundance measurements. We developed an approach to simultaneously measure biochemical activities and mRNA abundance in single cells to understand the heterogeneity of DNA repair activities across thousands of human lymphocytes, identifying known and novel cell-type-specific DNA repair phenotypes. Our approach provides a general framework for understanding functional heterogeneity among single cells.

scholarly journals Simultaneous measurement of biochemical phenotypes and gene expression in single cells

2020 ◽  
Vol 48 (10) ◽  
pp. e59-e59
Author(s):  
Amanda L Richer ◽  
Kent A Riemondy ◽  
Lakotah Hardie ◽  
Jay R Hesselberth
Keyword(s):  
Gene Expression ◽  
Dna Repair ◽  
Simultaneous Measurement ◽  
Single Cells ◽  
Human Lymphocytes ◽  
Mrna Abundance ◽  
Cell Type ◽  
Cell Type Specific

Abstract Methods to measure heterogeneity among cells are rapidly transforming our understanding of biology but are currently limited to molecular abundance measurements. We developed an approach to simultaneously measure biochemical activities and mRNA abundance in single cells to understand the heterogeneity of DNA repair activities across thousands of human lymphocytes, identifying known and novel cell-type-specific DNA repair phenotypes.

scholarly journals TUP1, CPH1 and EFG1 Make Independent Contributions to Filamentation in Candida albicans

Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Burkhard R Braun ◽  
Alexander D Johnson
Keyword(s):  
Candida Albicans ◽  
Environmental Conditions ◽  
Target Genes ◽  
Single Cells ◽  
Filamentous Growth ◽  
Surface Proteins ◽  
Pathway Expression ◽  
Separate Pathway ◽  
The Common ◽  
Growth Pathway

Abstract The common fungal pathogen, Candida albicans, can grow either as single cells or as filaments (hyphae), depending on environmental conditions. Several transcriptional regulators have been identified as having key roles in controlling filamentous growth, including the products of the TUP1, CPH1, and EFG1 genes. We show, through a set of single, double, and triple mutants, that these genes act in an additive fashion to control filamentous growth, suggesting that each gene represents a separate pathway of control. We also show that environmentally induced filamentous growth can occur even in the absence of all three of these genes, providing evidence for a fourth regulatory pathway. Expression of a collection of structural genes associated with filamentous growth, including HYR1, ECE1, HWP1, ALS1, and CHS2, was monitored in strains lacking each combination of TUP1, EFG1, and CPH1. Different patterns of expression were observed among these target genes, supporting the hypothesis that these three regulatory proteins engage in a network of individual connections to downstream genes and arguing against a model whereby the target genes are regulated through a central filamentous growth pathway. The results suggest the existence of several distinct types of filamentous forms of C. albicans, each dependent on a particular set of environmental conditions and each expressing a unique set of surface proteins.

scholarly journals Massively parallel gene expression variation measurement of a synonymous codon library

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Schmitz ◽  
Fuzhong Zhang
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Single Cells ◽  
Massively Parallel ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Change In Mean ◽  
Adaptation Index

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy

2014 ◽  
Vol 25 (22) ◽  
pp. 3699-3708 ◽  
Author(s):  
Anyimilehidi Mazo-Vargas ◽  
Heungwon Park ◽  
Mert Aydin ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Time Lapse ◽  
Photon Flux ◽  
Luminescence Microscopy ◽  
Cell Cycle Genes ◽  
Light Excitation ◽  
Better Than

Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15–20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.

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