Barcode beads allow efficient nucleic acid tagging in single cell genomics. Current barcode designs, however, are fabricated with a particular application in mind. Repurposing to novel targets, or altering to add additional targets as information is obtained is possible but the result is suboptimal. Here, we describe a modular framework that simplifies generation of multifunctional beads and allows their easy extension to new targets.

Current encapsulation approaches control the number of particles encapsulated per droplet, but not the particle types; consequently, they are unable to generate droplets containing combinations of distinct particle types, limiting the reactions that can be performed. We describe a microfluidic particle zipper that allows the number and types of particles encapsulated in every droplet to be controlled. The approach exploits self-ordering to generate repeating particle patterns that allow controlled encapsulation in droplets. We use the method to combine barcode particles with gel encapsulated cells to profile multiple disease relevant genomic loci with single cell sequencing. Particle zippers can operate in series to generate complex particle compositions in droplets.

The transcriptome and proteome encode distinct information that is important for characterizing heterogeneous biological systems. We demonstrate a method to simultaneously characterize the transcriptomes and proteomes of single cells at high throughput using aptamer probes and droplet-based single cell sequencing. With our method, we differentiate distinct cell types based on aptamer surface binding and gene expression patterns. Aptamers provide advantages over antibodies for single cell protein characterization, including rapid, in vitro, and high-purity generation via SELEX, and the ability to amplify and detect them with PCR and sequencing.

Studies of acute myeloid leukemia rely on DNA sequencing and immunophenotyping by flow cytometry as primary tools for disease characterization. However, leukemia tumor heterogeneity complicates integration of DNA variants and immunophenotypes from separate measurements. Here we introduce DAb-seq, a technology for simultaneous capture of DNA genotype and cell surface phenotype from single cells at high throughput, enabling direct profiling of proteogenomic states in tens of thousands of cells. To demonstrate the approach, we analyze the disease of three patients with leukemia over multiple treatment timepoints and disease recurrences. We observe complex genotype-phenotype dynamics that illustrate the subtlety of the disease process and the degree of incongruity between blast cell genotype and phenotype in different clinical scenarios. Our results highlight the importance of combined single-cell DNA and protein measurements to fully characterize the heterogeneity of leukemia.

Cell-cell interactions are important to numerous biological systems, including tissue microenvironments, the immune system, and cancer. However, current methods for studying cell combinations and interactions are limited in scalability, allowing just hundreds to thousands of multicell assays per experiment; this limited throughput makes it difficult to characterize interactions at biologically relevant scales. Here, we describe a paradigm in cell interaction profiling that allows accurate grouping of cells and characterization of their interactions for tens to hundreds of thousands of combinations. Our approach leverages high-throughput droplet microfluidics to construct multicellular combinations in a deterministic process that allows inclusion of programmed reagent mixtures and beads. The combination droplets are compatible with common manipulation and measurement techniques, including imaging, barcode-based genomics, and sorting. We demonstrate the approach by using it to enrich for chimeric antigen receptor (CAR)-T cells that activate upon incubation with target cells, a bottleneck in the therapeutic T cell engineering pipeline. The speed and control of our approach should enable valuable cell interaction studies.

Analyzing every cell in a diverse sample provides insight into population-level heterogeneity, but abundant cell types dominate the analysis and rarer populations are scarcely represented in the data. To focus on specific cell types, the current paradigm is to physically isolate subsets of interest prior to analysis; however, it remains difficult to isolate and then single-cell sequence such populations because of compounding losses. Here, we describe an alternative approach that selectively merges cells with reagents to achieve enzymatic reactions without having to physically isolate cells. We apply this technique to perform single-cell transcriptome and genome sequencing of specific cell subsets. Our method for analyzing heterogeneous populations obviates the need for pre- or post-enrichment and simplifies single-cell workflows, making it useful for other applications in single-cell biology, combinatorial chemical synthesis, and drug screening.

Schwann cell tumors are the most common cancers of the peripheral nervous system and can arise in patients with neurofibromatosis type-1 (NF-1) or neurofibromatosis type-2 (NF-2). Functional interactions between NF1 and NF2 and broader mechanisms underlying malignant transformation of the Schwann lineage are unclear. Here we integrate bulk and single-cell genomics, biochemistry, and pharmacology across human samples, cell lines, and mouse allografts to identify cellular de-differentiation mechanisms driving malignant transformation and treatment resistance. We find DNA methylation groups of Schwann cell tumors can be distinguished by differentiation programs that correlate with response to the MEK inhibitor selumetinib. Functional genomic screening in NF1-mutant tumor cells reveals NF2 loss and PAK activation underlie selumetinib resistance, and we find that concurrent MEK and PAK inhibition is effective in vivo. These data support a de-differentiation paradigm underlying malignant transformation and treatment resistance of Schwann cell tumors and elucidate a functional link between NF1 and NF2.

Myeloid malignancies, including acute myeloid leukaemia (AML), arise from the expansion of haematopoietic stem and progenitor cells that acquire somatic mutations. Bulk molecular profiling has suggested that mutations are acquired in a stepwise fashion: mutant genes with high variant allele frequencies appear early in leukaemogenesis, and mutations with lower variant allele frequencies are thought to be acquired later^1-3. Although bulk sequencing can provide information about leukaemia biology and prognosis, it cannot distinguish which mutations occur in the same clone(s), accurately measure clonal complexity, or definitively elucidate the order of mutations. To delineate the clonal framework of myeloid malignancies, we performed single-cell mutational profiling on 146 samples from 123 patients. Here we show that AML is dominated by a small number of clones, which frequently harbour co-occurring mutations in epigenetic regulators. Conversely, mutations in signalling genes often occur more than once in distinct subclones, consistent with increasing clonal diversity. We mapped clonal trajectories for each sample and uncovered combinations of mutations that synergized to promote clonal expansion and dominance. Finally, we combined protein expression with mutational analysis to map somatic genotype and clonal architecture with immunophenotype. Our findings provide insights into the pathogenesis of myeloid transformation and how clonal complexity evolves with disease progression.

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

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.