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Juvenile Dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I interferon response and autoantibodies. Treatment options are limited due to incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of JDM patients at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment towards an immature naive state as a hallmark of JDM at diagnosis. Furthermore, we find that these changes in B cells are paralleled by T cell signatures suggestive of Th2-mediated inflammation that persist despite disease quiescence. We applied network analysis to reveal that hyperactivation of the type I interferon response in all immune populations is coordinated with previously masked cell states including dysfunctional protein processing in CD4+ T cells and regulation of cell death programming in NK, CD8+ T cells and gdT cells. Together, these findings unveil the coordinated immune dysregulation underpinning JDM and provide insight into strategies for restoring balance in immune function.

Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

Although robustly expressed in the disease-free (DF) breast stroma, CD36 is consistently absent from the stroma surrounding invasive breast cancers (IBCs). In this study, we primarily observed CD36 expression in adipocytes and intralobular capillaries within the DF breast. Larger vessels concentrated in interlobular regions lacked CD36 and were instead marked by the expression of CD31. When evaluated in perilesional capillaries surrounding ductal carcinoma in situ, a nonobligate IBC precursor, CD36 loss was more commonly observed in lesions associated with subsequent IBC. Peroxisome proliferator-activated receptor γ (PPARγ) governs the expression of CD36 and genes involved in differentiation, metabolism, angiogenesis, and inflammation. Coincident with CD36 loss, we observed a dramatic suppression of PPARγ and its target genes in capillary endothelial cells (ECs) and pericytes, which typically surround and support the stability of the capillary endothelium. Factors present in conditioned media from malignant cells repressed PPARγ and its target genes not only in cultured ECs and pericytes but also in adipocytes, which require PPARγ for proper differentiation. In addition, we identified a role for PPARγ in opposing the transition of pericytes toward a tumor-supportive myofibroblast phenotype. In mouse xenograft models, early intervention with rosiglitazone, a PPARγ agonist, demonstrated significant antitumor effects; however, following the development of a palpable tumor, the antitumor effects of rosiglitazone were negated by the repression of PPARγ in the mouse stroma. In summary, PPARγ activity in healthy tissues places several stromal cell types in an antitumorigenic state, directly inhibiting EC proliferation, maintaining adipocyte differentiation, and suppressing the transition of pericytes into tumor-supportive myofibroblasts.

Introduction Metastases account for the vast majority of cancer-related deaths (1), however, why some cancers metastasize while others do not is poorly understood. Specific genetic alterations do not define metastatic [...], Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

The construction of three-dimensional (3D) microvascular networks with defined structures remains challenging. Emerging bioprinting strategies provide a means of patterning endothelial cells (ECs) into the geometry of 3D microvascular networks, but the microenvironmental cues necessary to promote their self-organization into cohesive and perfusable microvessels are not well known. To this end, we reconstituted microvessel formation in vitro by patterning thin lines of closely packed ECs fully embedded within a 3D extracellular matrix (ECM) and observed how different microenvironmental parameters influenced EC behaviors and their self-organization into microvessels. We found that the inclusion of fibrillar matrices, such as collagen I, into the ECM positively influenced cell condensation into extended geometries such as cords. We also identified the presence of a high-molecular-weight protein(s) in fetal bovine serum that negatively influenced EC condensation. This component destabilized cord structure by promoting cell protrusions and destabilizing cell-cell adhesions. Endothelial cords cultured in the presence of fibrillar collagen and in the absence of this protein activity were able to polarize, lumenize, incorporate mural cells, and support fluid flow. These optimized conditions allowed for the construction of branched and perfusable microvascular networks directly from patterned cells in as little as 3 days. These findings reveal important design principles for future microvascular engineering efforts based on bioprinting and micropatterning techniques. Impact statement Bioprinting is a potential strategy to achieve microvascularization in engineered tissues. However, the controlled self-organization of patterned endothelial cells into perfusable microvasculature remains challenging. We used DNA Programmed Assembly of Cells to create cell-dense, capillary-sized cords of endothelial cells with complete control over their structure. We optimized the matrix and media conditions to promote self-organization and maturation of these endothelial cords into stable and perfusable microvascular networks.

The gastrointestinal tract relies on the production, maturation, and transit of mucin to protect against pathogens and to lubricate the epithelial lining. Although the molecular and cellular mechanisms that regulate mucin production and movement are beginning to be understood, the upstream epithelial signals that contribute to mucin regulation remain unclear. Here, we report that the inflammatory cytokine tumor necrosis factor (TNF), generated by the epithelium, contributes to mucin homeostasis by regulating both cell differentiation and cystic fibrosis transmembrane conductance regulator (CFTR) activity. We used genetic mouse models and noninflamed samples from patients with inflammatory bowel disease (IBD) undergoing anti-TNF therapy to assess the effect of in vivo perturbation of TNF. We found that inhibition of epithelial TNF promotes the differentiation of secretory progenitor cells into mucus-producing goblet cells. Furthermore, TNF treatment and CFTR inhibition in intestinal organoids demonstrated that TNF promotes ion transport and luminal flow via CFTR. The absence of TNF led to slower gut transit times, which we propose results from increased mucus accumulation coupled with decreased luminal fluid pumping. These findings point to a TNF/CFTR signaling axis in the adult intestine and identify epithelial cell-derived TNF as an upstream regulator of mucin homeostasis.

Adherens junctions (AJs) create spatially, chemically and mechanically discrete microdomains at cellular interfaces. Here, using a mechanogenetic platform that generates artificial AJs with controlled protein localization, clustering and mechanical loading, we find that AJs also organize proteolytic hotspots for γ-secretase with a spatially regulated substrate selectivity that is critical in the processing of Notch and other transmembrane proteins. Membrane microdomains outside of AJs exclusively organize Notch ligand-receptor engagement (LRE microdomains) to initiate receptor activation. Conversely, membrane microdomains within AJs exclusively serve to coordinate regulated intramembrane proteolysis (RIP microdomains). They do so by concentrating γ-secretase and primed receptors while excluding full-length Notch. AJs induce these functionally distinct microdomains by means of lipid-dependent γ-secretase recruitment and size-dependent protein segregation. By excluding full-length Notch from RIP microdomains, AJs prevent inappropriate enzyme-substrate interactions and suppress spurious Notch activation. Ligand-induced ectodomain shedding eliminates size-dependent segregation, releasing Notch to translocate into AJs for processing by γ-secretase. This mechanism directs radial differentiation of ventricular zone-neural progenitor cells in vivo and more broadly regulates the proteolysis of other large cell-surface receptors such as amyloid precursor protein. These findings suggest an unprecedented role of AJs in creating size-selective spatial switches that choreograph γ-secretase processing of multiple transmembrane proteins regulating development, homeostasis and disease.

The rise and fall of estrogen and progesterone across menstrual cycles and during pregnancy regulates breast development and modifies cancer risk. How these hormones impact each cell type in the breast remains poorly understood because they act indirectly through paracrine networks. Using single-cell analysis of premenopausal breast tissue, we reveal a network of coordinated transcriptional programs representing the tissue-level response to changing hormone levels. Our computational approach, DECIPHER-seq, leverages person-to-person variability in breast composition and cell state to uncover programs that co-vary across individuals. We use differences in cell-type proportions to infer a subset of programs that arise from direct cell-cell interactions regulated by hormones. Further, we demonstrate that prior pregnancy and obesity modify hormone responsiveness through distinct mechanisms: obesity reduces the proportion of hormone-responsive cells, whereas pregnancy dampens the direct response of these cells to hormones. Together, these results provide a comprehensive map of the cycling human breast.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Enhancers integrate transcription factor signaling pathways that drive cell fate specification in the developing brain. We paired enhancer labeling and single-cell RNA-sequencing (scRNA-seq) to delineate and distinguish specification of neuronal lineages in mouse medial, lateral, and caudal ganglionic eminences (MGE, LGE, and CGE) at embryonic day (E)11.5. We show that scRNA-seq clustering using transcription factors improves resolution of regional and developmental populations, and that enhancer activities identify specific and overlapping GE-derived neuronal populations. First, we mapped the activities of seven evolutionarily conserved brain enhancers at single-cell resolution in vivo, finding that the selected enhancers had diverse activities in specific progenitor and neuronal populations across the GEs. We then applied enhancer-based labeling, scRNA-seq, and analysis of in situ hybridization data to distinguish transcriptionally distinct and spatially defined subtypes of MGE-derived GABAergic and cholinergic projection neurons and interneurons. Our results map developmental origins and specification paths underlying neurogenesis in the embryonic basal ganglia and showcase the power of scRNA-seq combined with enhancer-based labeling to resolve the complex paths of neuronal specification underlying mouse brain development.

SignificanceBisphenol A (BPA), found in many plastic products, has weak estrogenic effects that can be harmful to human health. Thus, structurally related replacements-bisphenol S (BPS) and bisphenol F (BPF)-are coming into wider use with very few data about their biological activities. Here, we compared the effects of BPA, BPS, and BPF on human mammary organoids established from normal breast tissue. BPS disrupted organoid architecture and induced supernumerary branching. At a proteomic level, the bisphenols altered the abundance of common targets and those that were unique to each compound. The latter included proteins linked to tumor-promoting processes. These data highlighted the importance of testing the human health effects of replacements that are structurally related to chemicals of concern.

DNA barcoding of individual cells combined with next-generation sequencing enables high-throughput parallel analysis of biomolecules at the single-cell level. Encoding protein identity with DNA barcoding of specific antibody binders achieves sequencing-based protein quantitation by converting protein signals into DNA signals. Here, we describe how to prepare DNA-barcoded antibodies and connect protein identities to cellular identities using droplet microfluidics. This approach allows for multiplex single-cell protein analysis compatible with single-cell transcriptomic and mutational profiling methods.

Microglia are resident macrophages in the brain that emerge in early development and respond to the local environment by altering their molecular and phenotypic states. Fundamental questions about microglia diversity and function during development remain unanswered because we lack experimental strategies to interrogate their interactions with other cell types and responses to perturbations ex vivo. We compared human microglia states across culture models, including cultured primary and pluripotent stem cell-derived microglia. We developed a "report card" of gene expression signatures across these distinct models to facilitate characterization of their responses across experimental models, perturbations, and disease conditions. Xenotransplantation of human microglia into cerebral organoids allowed us to characterize key transcriptional programs of developing microglia in vitro and reveal that microglia induce transcriptional changes in neural stem cells and decrease interferon signaling response genes. Microglia additionally accelerate the emergence of synchronized oscillatory network activity in brain organoids by modulating synaptic density.

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures.ResultsHere, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets.ConclusionsWe demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

EPH/EPHRIN signaling is essential to many aspects of tissue self-organization and morphogenesis, but little is known about how EPH/EPHRIN signaling regulates cell mechanics during these processes. Here, we use a series of approaches to examine how EPH/EPHRIN signaling drives cellular self-organization. Contact angle measurements reveal that EPH/EPHRIN signaling decreases the stability of heterotypic cell:cell contacts through increased cortical actomyosin contractility. We find that EPH/EPHRIN-driven cell segregation depends on actomyosin contractility but occurs independently of directed cell migration and without changes in cell adhesion. Atomic force microscopy and live cell imaging of myosin localization support that EPH/EPHRIN signaling results in increased cortical tension. Interestingly, actomyosin contractility also nonautonomously drives increased EPHB2:EPHB2 homotypic contacts. Finally, we demonstrate that changes in tissue organization are driven by minimization of heterotypic contacts through actomyosin contractility in cell aggregates and by mouse genetics experiments. These data elucidate the biomechanical mechanisms driving EPH/EPHRIN-based cell segregation wherein differences in interfacial tension, regulated by actomyosin contractility, govern cellular self-organization.

Embryonic tissue boundaries are critical to not only cement newly patterned structures during development, but also to serve as organizing centers for subsequent rounds of morphogenesis. ​Although this latter role is especially difficult to study in vivo, synthetic embryology offers a new vantage point and fresh opportunities. In this review, we cover recent progress towards understanding and controlling in vitro boundaries and how they impact synthetic model systems. A key point this survey highlights is that the outcome of self-organization is strongly dependent on the boundary imposed, and new insight into the complex functions of embryonic boundaries will be necessary to create better self-organizing tissues for basic science, drug development, and regenerative medicine.

Overexpressed tumor-associated antigens [for example, epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2)] are attractive targets for therapeutic T cells, but toxic "off-tumor" cross-reaction with normal tissues that express low levels of target antigen can occur with chimeric antigen receptor (CAR)-T cells. Inspired by natural ultrasensitive response circuits, we engineered a two-step positive-feedback circuit that allows human cytotoxic T cells to discriminate targets on the basis of a sigmoidal antigen-density threshold. In this circuit, a low-affinity synthetic Notch receptor for HER2 controls the expression of a high-affinity CAR for HER2. Increasing HER2 density thus has cooperative effects on T cells-it increases both CAR expression and activation-leading to a sigmoidal response. T cells with this circuit show sharp discrimination between target cells expressing normal amounts of HER2 and cancer cells expressing 100 times as much HER2, both in vitro and in vivo.

Droplet libraries consisting of many reagents encapsulated in separate droplets are necessary for applications of microfluidics, including combinatorial chemical synthesis, DNA-encoded libraries, and massively multiplexed PCR. However, existing approaches for generating them are laborious and impractical. Here, we describe an automated approach using a commercial array spotter. The approach can controllably emulsify hundreds of different reagents in a fraction of the time of manual operation of a microfluidic device, and without any user intervention. We demonstrate that the droplets produced by the spotter are similarly uniform to those produced by microfluidics and automate the generation of a ~ 2 mL emulsion containing 192 different reagents in ~ 4 h. The ease with which it can generate high diversity droplet libraries should make combinatorial applications more feasible in droplet microfluidics. Moreover, the instrument serves as an automated droplet generator, allowing execution of droplet reactions without microfluidic expertise.

The relative positioning of cells is a key feature of the microenvironment that organizes cell-cell interactions. To study the interactions between cells of the same or different type, micropatterning techniques have proved useful. DNA Programmed Assembly of Cells (DPAC) is a micropatterning technique that targets the adhesion of cells to a substrate or other cells using DNA hybridization. The most basic operations in DPAC begin with decorating cell membranes with lipid-modified oligonucleotides, then flowing them over a substrate that has been patterned with complementary DNA sequences. Cells adhere selectively to the substrate only where they find a complementary DNA sequence. Non-adherent cells are washed away, revealing a pattern of adherent cells. Additional operations include further rounds of cell-substrate or cell-cell adhesion, as well as transferring the patterns formed by DPAC to an embedding hydrogel for long-term culture. Previously, methods for patterning oligonucleotides on surfaces and decorating cells with DNA sequences required specialized equipment and custom DNA synthesis, respectively. We report an updated version of the protocol, utilizing an inexpensive benchtop photolithography setup and commercially available cholesterol modified oligonucleotides (CMOs) deployed using a modular format. CMO-labeled cells adhere with high efficiency to DNA-patterned substrates. This approach can be used to pattern multiple cell types at once with high precision and to create arrays of microtissues embedded within an extracellular matrix. Advantages of this method include its high resolution, ability to embed cells into a three-dimensional microenvironment without disrupting the micropattern, and flexibility in patterning any cell type.

Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis.

The mammary gland is a highly dynamic tissue that undergoes repeated cycles of growth and involution during pregnancy and menstruation. It is also the site from which breast cancers emerge. Organoids provide an in vitro model that preserves several of the cellular, structural, and microenvironmental features that dictate mammary gland function in vivo and have greatly advanced our understanding of glandular biology. Their tractability for genetic manipulation, live imaging, and high throughput screening have facilitated investigation into the mechanisms of glandular morphogenesis, structural maintenance, tumor progression, and invasion. Opportunities remain to enhance cellular and structural complexity of mammary organoid models, including incorporating additional cell types and hormone signaling.

Spatial transcriptomics seeks to integrate single cell transcriptomic data within the three-dimensional space of multicellular biology. Current methods to correlate a cell's position with its transcriptome in living tissues have various limitations. We developed an approach, called 'ZipSeq', that uses patterned illumination and photocaged oligonucleotides to serially print barcodes ('zipcodes') onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in vitro wound healing, live lymph node sections and a live tumor microenvironment. In all cases, we discovered new gene expression patterns associated with histological structures. In the tumor microenvironment, this demonstrated a trajectory of myeloid and T cell differentiation from the periphery inward. A combinatorial variation of ZipSeq efficiently scales in the number of regions defined, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

A promising molecular target for aggressive cancers is the urokinase receptor (uPAR). A fully human, recombinant antibody that binds uPAR to form a stable complex that blocks uPA-uPAR interactions (2G10) and is internalized primarily through endocytosis showed efficacy in a mouse xenograft model of highly aggressive, triple negative breast cancer (TNBC). Antibody-drug conjugates (ADCs) of 2G10 were designed and produced bearing tubulin inhibitor payloads ligated through seven different linkers. Aldehyde tag technology was employed for linking, and either one or two tags were inserted into the antibody heavy chain, to produce site-specifically conjugated ADCs with drug-to-antibody ratios of either two or four. Both cleavable and non-cleavable linkers were combined with two different antimitotic toxins-MMAE (monomethylauristatin E) and maytansine. Nine different 2G10 ADCs were produced and tested for their ability to target uPAR in cell-based assays and a mouse model. The anti-uPAR ADC that resulted in tumor regression comprised an MMAE payload with a cathepsin B cleavable linker, 2G10-RED-244-MMAE. This work demonstrates in vitro activity of the 2G10-RED-244-MMAE in TNBC cell lines and validates uPAR as a therapeutic target for TNBC.

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.

The cytokine IFN-γ is a critical regulator of immune system development and function. Almost all leukocytes express the receptor for IFN-γ, yet each cell type elicits a different response to this cytokine. Cell type-specific effects of IFN-γ make it difficult to predict the outcomes of the systemic IFN-γ blockade and limit its clinical application, despite many years of research. To better understand the cell-cell interactions and cofactors that specify IFN-γ functions, we focused on the function of IFN-γ on CD8 T cell differentiation. We demonstrated that during bacterial infection, IFN-γ is a dominant paracrine trigger that skews CD8 T cell differentiation toward memory. This skewing is preferentially driven by contact-dependent T cell-T cell (T-T) interactions and the localized IFN-γ secretion among activated CD8 T cells in a unique splenic microenvironment, and is less sensitive to concurrent IFN-γ production by other immune cell populations such as natural killer (NK) cells. Modulation of CD8 T cell differentiation by IFN-γ relies on a nonconventional IFN-γ outcome that occurs specifically within 24 hours following infection. This is driven by IFN-γ costimulation by integrins at T-T synapses, and leads to synergistic phosphorylation of the proximal STAT1 molecule and accelerated IL-2 receptor down-regulation. This study provides evidence of the importance of context-dependent cytokine signaling and gives another example of how cell clusters and the microenvironment drive unique biology.

Abstract: The human breast undergoes lifelong remodeling in response to estrogen and progesterone, but hormone exposure also increases breast cancer risk. Here, we use single-cell analysis to identify distinct mechanisms through which breast composition and cell state affect hormone signaling. We show that prior pregnancy reduces the transcriptional response of hormone-responsive (HR+) epithelial cells, whereas high body mass index (BMI) reduces overall HR+ cell proportions. These distinct changes both impact neighboring cells by effectively reducing the magnitude of paracrine signals originating from HR+ cells. Because pregnancy and high BMI are known to protect against hormone-dependent breast cancer in premenopausal women, our findings directly link breast cancer risk with person-to-person heterogeneity in hormone responsiveness. More broadly, our findings illustrate how cell proportions and cell state can collectively impact cell communities through the action of cell-to-cell signaling networks.

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

Droplet microfluidics enables massively-parallel analysis of single cells, biomolecules, and chemicals, making it valuable for high-throughput screens. However, many hydrophobic analytes are soluble in carrier oils, preventing their quantitative analysis with the method. We apply Printed Droplet Microfluidics to construct defined reactions with chemicals and cells incubated under air on an open array. The method interfaces with most bioanalytical tools and retains hydrophobic compounds in compartmentalized reactors, allowing their quantitation.

Growth factor binding to EGFR drives conformational changes that promote homodimerization and transphosphorylation, followed by adaptor recruitment, oligomerization, and signaling through Ras. Whether specific receptor conformations and oligomerization states are necessary for efficient activation of Ras is unclear. We therefore evaluated the sufficiency of a phosphorylated EGFR dimer to activate Ras without growth factor by developing a chemical-genetic strategy to crosslink and "trap" full-length EGFR homodimers on cells. Trapped dimers become phosphorylated and recruit adaptor proteins at stoichiometry equivalent to that of EGF-stimulated receptors. Surprisingly, these phosphorylated dimers do not activate Ras, Erk, or Akt. In the absence of EGF, phosphorylated dimers do not further oligomerize or reorganize on cell membranes. These results suggest that a phosphorylated EGFR dimer loaded with core signaling adapters is not sufficient to activate Ras and that EGFR ligands contribute to conformational changes or receptor dynamics necessary for oligomerization and efficient signal propagation through the SOS-Ras-MAPK pathway.

The biology of aging is challenging to study, particularly in humans. As a result, model organisms are used to approximate the physiological context of aging in humans. However, the best model organisms remain expensive and time-consuming to use. More importantly, they may not reflect directly on the process of aging in people. Human cell culture provides an alternative, but many functional signs of aging occur at the level of tissues rather than cells and are therefore not readily apparent in traditional cell culture models. Organoids have the potential to effectively balance between the strengths and weaknesses of traditional models of aging. They have sufficient complexity to capture relevant signs of aging at the molecular, cellular, and tissue levels, while presenting an experimentally tractable alternative to animal studies. Organoid systems have been developed to model many human tissues and diseases. Here we provide a perspective on the potential for organoids to serve as models for aging and describe how current organoid techniques could be applied to aging research.

Many tissues fold into complex shapes during development. Controlling this process in vitro would represent an important advance for tissue engineering. We use embryonic tissue explants, finite element modeling, and 3D cell-patterning techniques to show that mechanical compaction of the extracellular matrix during mesenchymal condensation is sufficient to drive tissue folding along programmed trajectories. The process requires cell contractility, generates strains at tissue interfaces, and causes patterns of collagen alignment around and between condensates. Aligned collagen fibers support elevated tensions that promote the folding of interfaces along paths that can be predicted by modeling. We demonstrate the robustness and versatility of this strategy for sculpting tissue interfaces by directing the morphogenesis of a variety of folded tissue forms from patterns of mesenchymal condensates. These studies provide insight into the active mechanical properties of the embryonic mesenchyme and establish engineering strategies for more robustly directing tissue morphogenesis ex vivo.

The mechanical properties of cells change with their differentiation, chronological age, and malignant progression. Consequently, these properties may be useful label-free biomarkers of various functional or clinically relevant cell states. Here, we demonstrate mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We define a new parameter, the whole-cell deformability index (wCDI), which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell types. The wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant, MCF-10A and BEAS-2B cell lines, and distinguishes between cells treated or untreated with cytoskeleton-perturbing small molecules. We categorize cell recovery time, ΔTr, as instantaneous (ΔTr ~ 0 ms), transient (ΔTr ≤ 40ms), or prolonged (ΔTr > 40ms), and show that the composition of recovery types, which is a consequence of changes in cytoskeletal organization, correlates with cellular transformation. Through the wCDI and cell-recovery time, mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry, but without antibody labeling. Mechano-NPS identifies mechanical phenotypes that distinguishes lineage, chronological age, and stage of malignant progression in human epithelial cells.

Spatiotemporal interrogation of signal transduction at the single-cell level is necessary to answer a host of important biological questions. This protocol describes a nanotechnology-based single-cell and single-molecule perturbation tool, termed mechanogenetics, that enables precise spatial and mechanical control over genetically encoded cell-surface receptors in live cells. The key components of this tool are a magnetoplasmonic nanoparticle (MPN) actuator that delivers defined spatial and mechanical cues to receptors through target-specific one-to-one engagement and a micromagnetic tweezers (μMT) that remotely controls the magnitude of force exerted on a single MPN. In our approach, a SNAP-tagged cell-surface receptor of interest is conjugated with a single-stranded DNA oligonucleotide, which hybridizes to its complementary oligonucleotide on the MPN. This protocol consists of four major stages: (i) chemical synthesis of MPNs, (ii) conjugation with DNA and purification of monovalent MPNs, (iii) modular targeting of MPNs to cell-surface receptors, and (iv) control of spatial and mechanical properties of targeted mechanosensitive receptors in live cells by adjusting the μMT-to-MPN distance. Using benzylguanine (BG)-functionalized MPNs and model cell lines expressing either SNAP-tagged Notch or vascular endothelial cadherin (VE-cadherin), we provide stepwise instructions for mechanogenetic control of receptor clustering and for mechanical receptor activation. The ability of this method to differentially control spatial and mechanical inputs to targeted receptors makes it particularly useful for interrogating the differential contributions of each individual cue to cell signaling. The entire procedure takes up to 1 week.

Although the elementary unit of biology is the cell, high-throughput methods for the microscale manipulation of cells and reagents are limited. The existing options either are slow, lack single-cell specificity, or use fluid volumes out of scale with those of cells. Here we present printed droplet microfluidics, a technology to dispense picoliter droplets and cells with deterministic control. The core technology is a fluorescence-activated droplet sorter coupled to a specialized substrate that together act as a picoliter droplet and single-cell printer, enabling high-throughput generation of intricate arrays of droplets, cells, and microparticles. Printed droplet microfluidics provides a programmable and robust technology to construct arrays of defined cell and reagent combinations and to integrate multiple measurement modalities together in a single assay.

Cells decide whether to grow and divide by integrating internal and external signals. Non-autonomous cell growth and proliferation occurs when microenvironmental signals from neighboring cells, both physical and secreted, license this decision. Understanding these processes is vital to developing an accurate framework for cell-cell interactions and cellular decision-making, and is useful for advancing new therapeutic strategies to prevent dysregulated growth. Here, we review some recent examples of non-autonomous cell growth in the mammary gland and tumor cell proliferation.

Proteins are the primary effectors of cellular function, including cellular metabolism, structural dynamics, and information processing. However, quantitative characterization of proteins at the single-cell level is challenging due to the tiny amount of protein available. Here, we present Abseq, a method to detect and quantitate proteins in single cells at ultrahigh throughput. Like flow and mass cytometry, Abseq uses specific antibodies to detect epitopes of interest; however, unlike these methods, antibodies are labeled with sequence tags that can be read out with microfluidic barcoding and DNA sequencing. We demonstrate this novel approach by characterizing surface proteins of different cell types at the single-cell level and distinguishing between the cells by their protein expression profiles. DNA-tagged antibodies provide multiple advantages for profiling proteins in single cells, including the ability to amplify low-abundance tags to make them detectable with sequencing, to use molecular indices for quantitative results, and essentially limitless multiplexing.

A T cell mounts an immune response by measuring the binding strength of its T cell receptor (TCR) for peptide-loaded MHCs (pMHC) on an antigen-presenting cell. How T cells convert the lifetime of the extracellular TCR-pMHC interaction into an intracellular signal remains unknown. Here, we developed a synthetic signaling system in which the extracellular domains of the TCR and pMHC were replaced with short hybridizing strands of DNA. Remarkably, T cells can discriminate between DNA ligands differing by a single base pair. Single-molecule imaging reveals that signaling is initiated when single ligand-bound receptors are converted into clusters, a time-dependent process requiring ligands with longer bound times. A computation model reveals that receptor clustering serves a kinetic proofreading function, enabling ligands with longer bound times to have disproportionally greater signaling outputs. These results suggest that spatial reorganization of receptors plays an important role in ligand discrimination in T cell signaling.

Purified populations of cells can be reconstituted into organoids that recapitulate aspects of their in vivo structure and function. These organoids are useful as models of healthy and diseased tissue in the basic sciences, in vitro screens, and regenerative medicine. Existing strategies to reconstitute organoids from purified cells face obstacles with respect to cell-viability, multicellular connectivity, scalability, and compatibility with subsequent experimental or analytical techniques. To address these challenges, we developed a strategy for rapidly casting populations of cells into microtissues of prescribed size and shape. This approach begins by chemically remodeling the adhesive properties of living cells with membrane-anchored ssDNA with modest annealing kinetics. Populations of complementary labeled cells are then combined into microwells that rapidly mold the DNA-adhesive cell populations into 3D aggregates of uniform size and shape. Once formed, aggregates are removed from the molds in the presence of "capping" oligonucleotides that block hybridization of residual surface DNA between aggregates in suspension. Finally, transfer of aggregates to biomimetic gels for 3D culture completes the process of reconstitution. This strategy of chemical micromolding allows for control over aggregate internal topology and does not perturb the natural process of self-organization in primary human mammary epithelial cells.

Polymeric microparticles can serve as carriers or sensors to instruct or characterize tissue biology. However, incorporating microparticles into tissues for in vitro assays remains a challenge. We exploit three-dimensional cell-patterning technologies and directed epithelial self-organization to deliver microparticles to the lumen of reconstituted human intestinal microtissues. We also develop a novel pH-sensitive microsensor that can measure the luminal pH of reconstituted epithelial microtissues. These studies offer a novel approach for investigating luminal microenvironments and drug-delivery across epithelial barriers.