In multi-cellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady-state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed pre-programmed cell-type hierarchy. These data demonstrate single-cell RNA-Seq as an effective tool for comprehensive cellular decomposition of complex tissues.

Within the bone marrow, stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with in vivo differentiation potential or gene regulatory mechanisms. Here, we comprehensively map myeloid progenitor subpopulations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups, showing unexpected transcriptional priming toward seven differentiation fates but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting that the process is tightly regulated. Histone maps and knockout assays are consistent with early transcriptional priming, while traditional transplantation experiments suggest that in vivo priming may still allow for plasticity given strong perturbations. These data establish a reference model and general framework for studying hematopoiesis at single-cell resolution.

Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the lowresolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.

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Metagenomic sequencing increased our understanding of the role of the microbiome in health and disease, yet it only provides a snapshot of a highly dynamic ecosystem. Here, we show that the pattern of metagenomic sequencing read coverage for different microbial genomes contains a single trough and a single peak, the latter coinciding with the bacterial origin of replication. Furthermore, the ratio of sequencing coverage between the peak and trough provides a quantitative measure of a species' growth rate. We demonstrate this in vitro and in vivo, under different growth conditions, and in complex bacterial communities. For several bacterial species, peak to trough coverage ratios, but not relative abundances, correlated with the manifestation of inflammatory bowel disease and type II diabetes.