Cell-to-cell variability in inducible Caspase9-mediated cell death

AbstractiCasp9 suicide gene has been widely used as a promising killing strategy in various cell therapies. However, different cells show significant heterogeneity in response to apoptosis inducer, posing challenges in clinical applications of killing strategy. The cause of the heterogeneity remains elusive so far. Here, by simultaneously monitoring the dynamics of iCasp9 dimerization, Caspase3 activation, and cell fate in single cells, we found that the heterogeneity was mainly due to cell-to-cell variability in initial iCasp9 expression and XIAP/Caspase3 ratio. Moreover, multiple-round drugging cannot increase the killing efficiency. Instead, it will place selective pressure on protein levels, especially on the level of initial iCasp9, leading to drug resistance. We further show this resistance can be largely eliminated by combinatorial drugging with XIAP inhibitor at the end, but not at the beginning, of the multiple-round treatments. Our results unveil the source of cell fate heterogeneity and drug resistance in iCasp9-mediated cell death, which may enlighten better therapeutic strategies for optimized killing.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription occurs often in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important issue: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that bust size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

FRET-FISH probes chromatin compaction at individual genomic loci in single cells

The density or compaction of chromatin throughout the cell nucleus is a key biophysical property that influences DNA replication, transcription, and repair. Chromatin accessibility is often used as a proxy for chromatin compaction or density, however it is not clear how these two properties relate to each other, given the lack of tools for directly probing compaction at defined genomic loci. To fill in this gap, here we developed FRET-FISH, a microscopy-based method combining fluorescence resonance energy transference (FRET) with DNA fluorescence in situ hybridization (FISH) to probe chromatin compaction at selected loci in single cells. We optimized FRET-FISH by testing different probe designs in situ in fixed cells, readily detecting FRET generated by DNA FISH probes. To validate FRET-FISH, we compared it with ATAC-seq and Hi-C, demonstrating that local chromatin compaction and accessibility are strongly correlated and that the frequency of intra-genic contacts measured by Hi-C may be an even better proxy for local chromatin density. To further validate FRET-FISH, we showed that it can detect expected differences in chromatin compaction along the nuclear radius, with peripheral loci being more compacted and central ones less compacted. Lastly, we assessed the sensitivity of FRET-FISH, demonstrating its ability to reproducibly detect differences in chromatin density (i) upon treatment of cells with drugs that perturb global chromatin condensation; (ii) during prolonged cell culture; and (iii) in different phases of the cell cycle. We conclude that FRET-FISH is a robust tool for probing chromatin compaction at selected loci in single cells and for studying inter-allelic and cell-to-cell variability in chromatin density.

Age-dependent aggregation of ribosomal RNA-binding proteins links deterioration in chromatin stability with loss of proteostasis

SummaryChromatin instability and loss of protein homeostasis (proteostasis) are two well-established hallmarks of aging, which have been considered largely independent of each other. Using microfluidics and single-cell imaging approaches, we observed that, during the replicative aging of S.cerevisiae, proteostasis decline occurred specifically in the fraction of cells with decreased stability at the ribosomal DNA (rDNA) region. A screen of 170 yeast RNA-binding proteins identified ribosomal RNA (rRNA)- binding proteins as the most enriched group that aggregate upon a decrease in rDNA stability. We further found that loss of rDNA stability contributes to age-dependent aggregation of rRNA-binding proteins through aberrant overproduction of rRNAs. These aggregates negatively impact nucleolar integrity and global proteostasis and hence limit cellular lifespan. Our findings reveal a mechanism underlying the interconnection between chromatin instability and proteostasis decline and highlight the importance of cell-to-cell variability in aging processes.

Identifying cell-to-cell variability in internalisation using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is the primary means by which cells absorb drugs, viruses and other material, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data collected from an experiment that probes the internalisation of antibody by transferrin receptors in C1R cells. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from experiments that probe a range of dynamical processes.

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single cell clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell–cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space. The source code and usage information are available at https://github.com/Snehalikalall/CopulaGCN .

Pathway dynamics can delineate the sources of transcriptional noise in gene expression

Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells ('intrinsic noise') from variability across the population ('extrinsic noise'). Here we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that 'pathway-reporters' compare favourably to the well-known, but often difficult to implement, dual-reporter method.

Multiscale and integrative single-cell Hi-C analysis with Higashi

Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.