Oligonucleotides are almost exclusively synthesized using the nucleoside phosphoramidite method, even though it is limited to the direct synthesis of ∼200 mers and produces hazardous waste. Here, we describe an oligonucleotide synthesis strategy that uses the template-independent polymerase terminal deoxynucleotidyl transferase (TdT). Each TdT molecule is conjugated to a single deoxyribonucleoside triphosphate (dNTP) molecule that it can incorporate into a primer. After incorporation of the tethered dNTP, the 3' end of the primer remains covalently bound to TdT and is inaccessible to other TdT-dNTP molecules. Cleaving the linkage between TdT and the incorporated nucleotide releases the primer and allows subsequent extension. We demonstrate that TdT-dNTP conjugates can quantitatively extend a primer by a single nucleotide in 10-20 s, and that the scheme can be iterated to write a defined sequence. This approach may form the basis of an enzymatic oligonucleotide synthesizer.

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell–cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell–cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell–cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

Cell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans’ cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.Graphical abstract

Multiphoton microscopy is a powerful technique for deep in vivo imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to generate contrast in scattering tissue, while minimizing photobleaching and phototoxicity. We show here how adaptive imaging can optimize illumination power at each point in a 3D volume as a function of the sample’s shape, without the need for specialized fluorescent labeling. Our method relies on training a physics-based machine learning model using cells with identical fluorescent labels imaged in situ. We use this technique for in vivo imaging of immune responses in mouse lymph nodes following vaccination. We achieve visualization of physiologically realistic numbers of antigen-specific T cells (~2 orders of magnitude lower than previous studies), and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response. We provide a step-by-step tutorial for implementing this technique using exclusively open-source hardware and software.

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which is the first strategy to decipher context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand receptor pairs) linked to COVID-19 severities. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans’ body. Leveraging a 3D atlas of C. elegans’ cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

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Background Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging. Results Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3′ shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation. Conclusion The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. Graphical abstract