Cell Systems

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Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes...

The connection between growth and gene expression has often been considered in a single gene. Repurposing a drug-drug interaction model, the multidimensional effects of several simultaneous gene expression perturbations on growth have been examined in the model bacteria Escherichia coli.

Understanding the fitness of protein variants with combinatorial mutations is critical for effective protein engineering. In this issue of Cell Systems, Chu et al. present TopVIP, a top variant identification pipeline that enables accurate picking of the greatest number of best-performing protein variants with high-fitness leveraging zero-shot predictor and low-N iterative sampling.

Protein clustering plays numerous roles in cell physiology and disease. However, protein oligomers can be difficult to detect because they are often too small to appear as puncta in conventional fluorescence microscopy. Here, we describe a fluorescent reporter strategy that detects protein clusters with high sensitivity called CluMPS (clusters magnified by phase separation). A CluMPS reporter detects and visually amplifies even small clusters of a binding partner, generating large, quantifiable fluorescence condensates...

In microbial systems, a metabolic pathway can be either completed by one autonomous population or distributed among a consortium performing metabolic division of labor (MDOL). MDOL facilitates the system's function by reducing the metabolic burden; however, it may hinder the function by reducing the exchange efficiency of metabolic intermediates among individuals. As a result, the function of a community is influenced by the trade-offs between the metabolic specialization and versatility of individuals. To experimentally test this hypothesis, we deconstructed the naphthalene degradation pathway into four steps and introduced them individually or combinatorically into different strains with varying levels of metabolic specialization...

Synthetic minimal cells are a class of bioreactors that have some, but not all, functions of live cells. Here, we report a critical step toward the development of a bottom-up minimal cell: cellular export of functional protein and RNA products. We used cell-penetrating peptide tags to translocate payloads across a synthetic cell vesicle membrane. We demonstrated efficient transport of active enzymes and transport of nucleic acid payloads by RNA-binding proteins. We investigated influence of a concentration gradient alongside other factors on the efficiency of the translocation, and we show a method to increase product accumulation in one location...

Signaling pathways feature multiple interacting ligand and receptor variants, which are thought to act in a combinatorial manner to elicit different cellular responses. Transcriptome analyses now suggest that many signaling pathways use their components in combinations that are surprisingly often shared between otherwise dissimilar cell states.

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters...

Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering...

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions...

To examine how bacteria achieve robust cell proliferation across diverse conditions, we developed a method that quantifies 77 cell morphological, cell cycle, and growth phenotypes of a fluorescently labeled Escherichia coli strain and >800 gene deletion derivatives under multiple nutrient conditions. This approach revealed extensive phenotypic plasticity and deviating mutant phenotypes were often nutrient dependent. From this broad phenotypic landscape emerged simple and robust unifying rules (laws) that connect DNA replication initiation, nucleoid segregation, FtsZ ring formation, and cell constriction to specific aspects of cell size (volume, length, or added length) at the population level...

The efficacy of epitope vaccines depends on the included epitopes as well as the probability that the selected epitopes are presented by the major histocompatibility complex (MHC) proteins of a vaccinated individual. Designing vaccines that effectively immunize a high proportion of the population is challenging because of high MHC polymorphism, diverging MHC-peptide binding affinities, and physical constraints on epitope vaccine constructs. Here, we present HOGVAX, a combinatorial optimization approach for epitope vaccine design...

CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations...

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors...

Single-cell data and computational simulations reveal the dynamics of the transcription factors HIF1α and PPARγ during adipocyte differentiation and maturation. Modeling feedback within this network predicts a HIF1α-mediated choice between lipid accumulation and incomplete differentiation. In vitro experiments support this model, with implications for adipose dynamics in metabolic disorders involving hypoxia.

When a system robustly corrects component-level errors, the direct pressure on component performance declines. Components become less reliable, maintain more genetic variability, or drift neutrally, creating new forms of complexity. Examples include the hourglass pattern of biological development and the hourglass architecture for robustly complex systems in engineering.

Stem cells differentiate into distinct fates by transitioning through a series of transcriptional states. Current computational approaches allow reconstruction of differentiation trajectories from single-cell transcriptomics data, but it remains unknown to what degree differentiation can be predicted across biological processes. Here, we use transfer learning to infer differentiation processes and quantify predictability in early embryonic development and adult hematopoiesis. Overall, we find that non-linear methods outperform linear approaches, and we achieved the best predictions with a custom variational autoencoder that explicitly models changes in transcriptional variance...

Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types...

Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states...