Cell Systems最新文献

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

Optogenetics enables the induction of virtual stress, which separates stress signaling from cellular damage. This provides new insights into the dynamics of the integrated stress response and reveals the mechanisms through which cells form memories of past stress events to guide their response to acute stress.

The integrated stress response (ISR) is a conserved signaling network that detects aberrations and computes cellular responses. Dissecting these computations has been difficult because physical and chemical inducers of stress activate multiple parallel pathways. To overcome this challenge, we engineered a photo-switchable control over the ISR sensor kinase PKR (opto-PKR), enabling virtual, on-target activation. Using light to control opto-PKR dynamics, we traced information flow through the transcriptome and for key downstream ISR effectors. Our analyses revealed a biphasic, proportional transcriptional response with two dynamic modes, transient and gradual, that correspond to adaptive and terminal outcomes. We then constructed an ordinary differential equation (ODE) model of the ISR, which demonstrated the dependence of future stress responses on past stress. Finally, we tested our model using high-throughput light-delivery to map the stress memory landscape. Our results demonstrate that cells encode information in stress levels, durations, and the timing between encounters. A record of this paper's transparent peer review process is included in the supplemental information.

How β-catenin, the nuclear activator of the Wnt pathway, affects the chromatin environment of its targets is unknown. Over a time course of stimulation, β-catenin repositions itself around the genome in a cell-type-specific manner, eliciting transient chromatin changes in differentiated cells and progressive shaping of undifferentiated cells.

Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source.

Single-cell RNA sequencing (scRNA-seq) massively profiles transcriptomes of individual cells encapsulated in barcoded droplets in parallel. However, in real-world scRNA-seq data, many barcoded droplets do not contain cells, but instead, they capture a fraction of ambient RNAs released from damaged or lysed cells. A typical first step to analyze scRNA-seq data is to filter out cell-free droplets and isolate cell-containing droplets, but distinguishing them is often challenging; incorrect filtering may mislead the downstream analysis substantially. We propose SiftCell, a suite of software tools to identify and visualize cell-containing and cell-free droplets in manifold space via randomization (SiftCell-Shuffle) to classify between the two types of droplets (SiftCell-Boost) and to quantify the contribution of ambient RNAs for each droplet (SiftCell-Mix). By applying our method to datasets obtained by various single-cell platforms, we show that SiftCell provides a streamlined way to perform upstream quality control of scRNA-seq, which is more comprehensive and accurate than existing methods.

Genotoxic stress in mammalian cells, including those caused by anti-cancer chemotherapy, can induce temporary cell-cycle arrest, DNA damage-induced senescence (DDIS), or apoptotic cell death. Despite obvious clinical importance, it is unclear how the signals emerging from DNA damage are integrated together with other cellular signaling pathways monitoring the cell's environment and/or internal state to control different cell fates. Using single-cell-based signaling measurements combined with tensor partial least square regression (t-PLSR)/principal component analysis (PCA) analysis, we show that JNK and Erk MAPK signaling regulates the initiation of cell senescence through the transcription factor AP-1 at early times after doxorubicin-induced DNA damage and the senescence-associated secretory phenotype (SASP) at late times after damage. These results identify temporally distinct roles for signaling pathways beyond the classic DNA damage response (DDR) that control the cell senescence decision and modulate the tumor microenvironment and reveal fundamental similarities between signaling pathways responsible for oncogene-induced senescence (OIS) and senescence caused by topoisomerase II inhibition. A record of this paper's transparent peer review process is included in the supplemental information.

Wnt signaling orchestrates gene expression via its effector, β-catenin. However, it is unknown whether β-catenin binds its target genomic regions simultaneously and how this impacts chromatin dynamics to modulate cell behavior. Using a combination of time-resolved CUT&RUN against β-catenin, ATAC-seq, and perturbation assays in different cell types, we show that Wnt/β-catenin physical targets are tissue-specific, β-catenin "moves" on different loci over time, and its association to DNA accompanies changing chromatin accessibility landscapes that determine cell behavior. In particular, Wnt/β-catenin progressively shapes the chromatin of human embryonic stem cells (hESCs) as they undergo mesodermal differentiation, a behavior that we define as "plastic." In HEK293T cells, on the other hand, Wnt/β-catenin drives a transient chromatin opening, followed by re-establishment of the pre-stimulation state, a response that we define as "elastic." Future experiments shall assess whether other cell communication mechanisms, in addition to Wnt signaling, are ruled by time, cellular idiosyncrasies, and chromatin constraints. A record of this paper's transparent peer review process is included in the supplemental information.