During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background, or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. A record of this paper's transparent peer review process is included in the supplemental information.

Control of dynamical processes is vital for maintaining correct cell regulation and cell-fate decisions. Numerous regulatory networks show oscillatory behavior; however, our knowledge of how one oscillator behaves when stimulated by two or more external oscillatory signals is still missing. We explore this problem by constructing a synthetic oscillatory system in yeast and stimulate it with two external oscillatory signals. Letting model verification and prediction operate in a tight interplay with experimental observations, we find that stimulation with two external signals expands the plateau of entrainment and reduces the fluctuations of oscillations. Furthermore, by adjusting the phase differences of external signals, one can control the amplitude of oscillations, which is understood through the signal delay of the unperturbed oscillatory network. With this we reveal a direct amplitude dependency of downstream gene transcription. Taken together, these results suggest a new path to control oscillatory systems by coupled oscillator cooperativity.

Selecting the most suitable existing base editors and engineering new variants for installing specific base conversions with maximal efficiency and minimal undesired edits are pivotal for precise genome editing applications. Here, we present a platform for creating and analyzing a library of engineered base editor variants to enable head-to-head evaluation of their editing performance at scale. Our comprehensive comparison provides quantitative measures on each variant's editing efficiency, purity, motif preference, and bias in generating single and multiple base conversions, while uncovering undesired higher indel generation rate and noncanonical base conversion for some of the existing base editors. In addition to engineering the base editor protein, we further applied this platform to investigate a hitherto underexplored engineering route and created guide RNA scaffold variants that augment the editor's base-editing activity. With the unknown performance and compatibility of the growing number of engineered parts including deaminase, CRISPR-Cas enzyme, and guide RNA scaffold variants for assembling the expanding collection of base editor systems, our platform addresses the unmet need for an unbiased, scalable method to benchmark their editing outcomes and accelerate the engineering of next-generation precise genome editors.

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

Modeling systems at multiple interacting scales is probably the most relevant task for pursuing a physically motivated explanation of biological regulation. In a new study, Smart and Zilman develop a convincing, albeit preliminary, model of the interplay between the cell microscale and the macroscopic tissue organization in biological systems.

Allosteric regulation is central to protein function in cellular networks. A fundamental open question is whether cellular regulation of allosteric proteins occurs only at a few defined positions or at many sites distributed throughout the structure. Here, we probe the regulation of GTPases-protein switches that control signaling through regulated conformational cycling-at residue-level resolution by deep mutagenesis in the native biological network. For the GTPase Gsp1/Ran, we find that 28% of the 4,315 assayed mutations show pronounced gain-of-function responses. Twenty of the sixty positions enriched for gain-of-function mutations are outside the canonical GTPase active site switch regions. Kinetic analysis shows that these distal sites are allosterically coupled to the active site. We conclude that the GTPase switch mechanism is broadly sensitive to cellular allosteric regulation. Our systematic discovery of new regulatory sites provides a functional map to interrogate and target GTPases controlling many essential biological processes.

Maintaining persistent migration in complex environments is critical for neutrophils to reach infection sites. Neutrophils avoid getting trapped, even when obstacles split their front into multiple leading edges. How they re-establish polarity to move productively while incorporating receptor inputs under such conditions remains unclear. Here, we challenge chemotaxing HL60 neutrophil-like cells with symmetric bifurcating microfluidic channels to probe cell-intrinsic processes during the resolution of competing fronts. Using supervised statistical learning, we demonstrate that cells commit to one leading edge late in the process, rather than amplifying structural asymmetries or early fluctuations. Using optogenetic tools, we show that receptor inputs only bias the decision similarly late, once mechanical stretching begins to weaken each front. Finally, a retracting edge commits to retraction, with ROCK limiting sensitivity to receptor inputs until the retraction completes. Collectively, our results suggest that cell edges locally adopt highly stable protrusion/retraction programs that are modulated by mechanical feedback.

A new method developed by Francisco Quintana's group, systematic perturbation of encapsulated associated cells followed by sequencing (SPEAC-seq), applies a CRISPR screen to co-cultured interacting cells to identify the ligands mediating cell-cell communication. Using this approach, the authors discover the molecular basis of a microglia-astrocyte feedback loop that suppresses neuroinflammatory disease.

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

The transcriptional effector domains of transcription factors play a key role in controlling gene expression; however, their functional nature is poorly understood, hampering our ability to explore this fundamental dimension of gene regulatory networks. To map the trans-regulatory landscape in a complex eukaryote, we systematically characterized the putative transcriptional effector domains of over 400 Arabidopsis thaliana transcription factors for their capacity to modulate transcription. We demonstrate that transcriptional effector activity can be integrated into gene regulatory networks capable of elucidating the functional dynamics underlying gene expression patterns. We further show how our characterized domains can enhance genome engineering efforts and reveal how plant transcriptional activators share regulatory features conserved across distantly related eukaryotes. Our results provide a framework to systematically characterize the regulatory role of transcription factors at a genome-scale in order to understand the transcriptional wiring of biological systems.

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for describing cell states. Identifying the spatial arrangement of these states in tissues remains challenging, with the existing methods requiring niche methodologies and expertise. Here, we describe segmentation by exogenous perfusion (SEEP), a rapid and integrated method to link surface proximity and environment accessibility to transcriptional identity within three-dimensional (3D) disease models. The method utilizes the steady-state diffusion kinetics of a fluorescent dye to establish a gradient along the radial axis of disease models. Classification of sample layers based on dye accessibility enables dissociated and sorted cells to be characterized by transcriptomic and regional identities. Using SEEP, we analyze spheroid, organoid, and in vivo tumor models of high-grade serous ovarian cancer (HGSOC). The results validate long-standing beliefs about the relationship between cell state and position while revealing new concepts regarding how spatially unique microenvironments influence the identity of individual cells within tumors.

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Viruses encode transcriptional regulatory proteins critical for controlling viral and host gene expression. Given their multifunctional nature and high sequence divergence, it is unclear which viral proteins can affect transcription and which specific sequences contribute to this function. Using a high-throughput assay, we measured the transcriptional regulatory potential of over 60,000 protein tiles across ∼1,500 proteins from 11 coronaviruses and all nine human herpesviruses. We discovered hundreds of transcriptional effector domains, including a conserved repression domain in all coronavirus Spike homologs, dual activation-repression domains in viral interferon regulatory factors (VIRFs), and an activation domain in six herpesvirus homologs of the single-stranded DNA-binding protein that we show is important for viral replication and late gene expression in Kaposi's sarcoma-associated herpesvirus (KSHV). For the effector domains we identified, we investigated their mechanisms via high-throughput sequence and chemical perturbations, pinpointing sequence motifs essential for function. This work massively expands viral protein annotations, serving as a springboard for studying their biological and health implications and providing new candidates for compact gene regulation tools.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

To build therapeutic strains, Escherichia coli Nissle (EcN) have been engineered to express antibiotics, toxin-degrading enzymes, immunoregulators, and anti-cancer chemotherapies. For efficacy, the recombinant genes need to be highly expressed, but this imposes a burden on the cell, and plasmids are difficult to maintain in the body. To address these problems, we have developed landing pads in the EcN genome and genetic circuits to control therapeutic gene expression. These tools were applied to EcN SYNB1618, undergoing clinical trials as a phenylketonuria treatment. The pathway for converting phenylalanine to trans-cinnamic acid was moved to a landing pad under the control of a circuit that keeps the pathway off during storage. The resulting strain (EcN SYN8784) achieved higher activity than EcN SYNB1618, reaching levels near when the pathway is carried on a plasmid. This work demonstrates a simple system for engineering EcN that aids quantitative strain design for therapeutics.

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

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.

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.

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.