High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding.

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time.

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes.

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation.

Spatial and cell type transcriptional landscape of human cerebellar development.

The human neonatal cerebellum is one-fourth of its adult size yet contains the blueprint required to integrate environmental cues with developing motor, cognitive and emotional skills into adulthood. Although mature cerebellar neuroanatomy is well studied, understanding of its developmental origins is limited. In this study, we systematically mapped the molecular, cellular and spatial composition of human fetal cerebellum by combining laser capture microscopy and SPLiT-seq single-nucleus transcriptomics.

Microbial single-cell RNA sequencing by split-pool barcoding.

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle.

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity.

Redefining the Etiologic Landscape of Cerebellar Malformations.

Cerebellar malformations are diverse congenital anomalies frequently associated with developmental disability. Although genetic and prenatal non-genetic causes have been described, no systematic analysis has been performed. Here, we present a large-exome sequencing study of Dandy-Walker malformation (DWM) and cerebellar hypoplasia (CBLH).

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters.

Degenerate minigene library analysis enables identification of altered branch point utilization by mutant splicing factor 3B1 (SF3B1).

Cancer-associated mutations of the core splicing factor 3 B1 (SF3B1) result in selection of novel 3' splice sites (3'SS), but precise molecular mechanisms of oncogenesis remain unclear. SF3B1 stabilizes the interaction between U2 snRNP and branch point (BP) on the pre-mRNA. It has hence been speculated that a change in BP selection is the basis for novel 3'SS selection.

Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.

To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords.

Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences.

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of -regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure.

Learning the sequence determinants of alternative splicing from millions of random sequences.

Most human transcripts are alternatively spliced, and many disease-causing mutations affect RNA splicing. Toward better modeling the sequence determinants of alternative splicing, we measured the splicing patterns of over two million (M) synthetic mini-genes, which include degenerate subsequences totaling over 100 M bases of variation. The massive size of these training data allowed us to improve upon current models of splicing, as well as to gain new mechanistic insights.

MicroRNA-based single-gene circuits buffer protein synthesis rates against perturbations.

Achieving precise control of mammalian transgene expression has remained a long-standing, and increasingly urgent, challenge in biomedical science. Despite much work, single-cell methods have consistently revealed that mammalian gene expression levels remain susceptible to fluctuations (noise) and external perturbations. Here, we show that precise control of protein synthesis can be realized using a single-gene microRNA (miRNA)-based feed-forward loop (sgFFL).