Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens

Integrating Time-Resolved nrf2 Gene-Expression Data into a Full GUTS Model as a Proxy for Toxicodynamic Damage in Zebrafish Embryo

The immense production of the chemical industry requires an improved predictive risk assessment that can handle constantly evolving challenges while reducing the dependency of risk assessment on animal testing. Integrating omics data into mechanistic models offers a promising solution by linking cellular processes triggered after chemical exposure with observed effects in the organism. With the emerging availability of time-resolved RNA data, the goal of integrating gene expression data into mechanistic models can be approached. We propose a biologically anchored TKTD model, which describes key processes that link the gene expression level of the stress regulator nrf2 to detoxification and lethality by associating toxicodynamic damage with nrf2 expression. Fitting such a model to complex data sets consisting of multiple endpoints required the combination of methods from molecular biology, mechanistic dynamic systems modeling, and Bayesian inference. In this study, we successfully integrate time-resolved gene expression data into TKTD models and thus provide a method for assessing the influence of molecular markers on survival. This novel method was used to test whether nrf2 can be applied to predict lethality in zebrafish embryos. With the presented approach, we outline a method to successfully approach the goal of a predictive risk assessment based on molecular data.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Biophysically interpretable inference of cell types from multimodal sequencing data

Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or 'clusters' in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for 'clusters' through the governing parameters of cellular processes.

Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.

Central dogma rates in human mitochondria

In human cells, the nuclear and mitochondrial genomes engage in a complex interplay to produce dual-encoded oxidative phosphorylation (OXPHOS) complexes. The coordination of these dynamic gene expression processes is essential for producing matched amounts of OXPHOS protein subunits. This review focuses on our current understanding of the mitochondrial central dogma rates, highlighting the striking differences in gene expression rates between mitochondrial and nuclear genes. We synthesize a coherent model of mitochondrial gene expression kinetics, highlighting the emerging principles and emphasizing where more precise measurements would be beneficial. Such an understanding is pivotal for grasping the unique aspects of mitochondrial function and its role in cellular energetics, and it has profound implications for aging, metabolic disorders, and neurodegenerative diseases.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.