Oncogenic Notch Promotes Long-Range Regulatory Interactions within Hyperconnected 3D Cliques.

Chromatin loops enable transcription-factor-bound distal enhancers to interact with their target promoters to regulate transcriptional programs. Although developmental transcription factors such as active forms of Notch can directly stimulate transcription by activating enhancers, the effect of their oncogenic subversion on the 3D organization of cancer genomes is largely undetermined. By mapping chromatin looping genome-wide in Notch-dependent triple-negative breast cancer and B cell lymphoma, we show that beyond the well-characterized role of Notch as an activator of distal enhancers, Notch regulates its direct target genes by instructing enhancer repositioning. Moreover, a large fraction of Notch-instructed regulatory loops form highly interacting enhancer and promoter spatial clusters termed "3D cliques." Loss- and gain-of-function experiments show that Notch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-oncogenes. Our observations suggest that oncogenic hijacking of developmental transcription factors can dysregulate transcription through widespread effects on the spatial organization of cancer genomes.

Predicting and Manipulating Cone Responses to Naturalistic Inputs.

Primates explore their visual environment by making frequent saccades, discrete and ballistic eye movements that direct the fovea to specific regions of interest. Saccades produce large and rapid changes in input. The magnitude of these changes and the limited signaling range of visual neurons mean that effective encoding requires rapid adaptation. Here, we explore how macaque cone photoreceptors maintain sensitivity under these conditions. Adaptation makes cone responses to naturalistic stimuli highly nonlinear and dependent on stimulus history. Such responses cannot be explained by linear or linear-nonlinear models but are well explained by a biophysical model of phototransduction based on well-established biochemical interactions. The resulting model can predict cone responses to a broad range of stimuli and enables the design of stimuli that elicit specific (e.g., linear) cone photocurrents. These advances will provide a foundation for investigating the contributions of cone phototransduction and post-transduction processing to visual function.SIGNIFICANCE STATEMENT We know a great deal about adaptational mechanisms that adjust sensitivity to slow changes in visual inputs such as the rising or setting sun. We know much less about the rapid adaptational mechanisms that are essential for maintaining sensitivity as gaze shifts around a single visual scene. We characterize how phototransduction in cone photoreceptors adapts to rapid changes in input similar to those encountered during natural vision. We incorporate these measurements into a quantitative model that can predict cone responses across a broad range of stimuli. This model not only shows how cone phototransduction aids the encoding of natural inputs but also provides a tool to identify the role of the cone responses in shaping those of downstream visual neurons.

TooManyCells identifies and visualizes relationships of single-cell clades.

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

Optimal Burstiness in Populations of Spiking Neurons Facilitates Decoding of Decreases in Tonic Firing.

A stimulus can be encoded in a population of spiking neurons through any change in the statistics of the joint spike pattern, yet we commonly summarize single-trial population activity by the summed spike rate across cells: the population peristimulus time histogram (pPSTH). For neurons with a low baseline spike rate that encode a stimulus with a rate increase, this simplified representation works well, but for populations with high baseline rates and heterogeneous response patterns, the pPSTH can obscure the response. We introduce a different representation of the population spike pattern, which we call an "information train," that is well suited to conditions of sparse responses, especially those that involve decreases rather than increases in firing. We use this tool to study populations with varying levels of burstiness in their spiking statistics to determine how burstiness affects the representation of spike decreases (firing "gaps"). Our simulated populations of spiking neurons varied in size, baseline rate, burst statistics, and correlation. Using the information train decoder, we find that there is an optimal level of burstiness for gap detection that is robust to several other parameters of the population. We consider this theoretical result in the context of experimental data from different types of retinal ganglion cells and determine that the baseline spike statistics of a recently identified type support nearly optimal detection of both the onset and strength of a contrast step.

Three Small-Receptive-Field Ganglion Cells in the Mouse Retina Are Distinctly Tuned to Size, Speed, and Object Motion.

Retinal ganglion cells (RGCs) are frequently divided into functional types by their ability to extract and relay specific features from a visual scene, such as the capacity to discern local or global motion, direction of motion, stimulus orientation, contrast or uniformity, or the presence of large or small objects. Here we introduce three previously uncharacterized, nondirection-selective ON-OFF RGC types that represent a distinct set of feature detectors in the mouse retina. The three high-definition (HD) RGCs possess small receptive-field centers and strong surround suppression. They respond selectively to objects of specific sizes, speeds, and types of motion. We present comprehensive morphological characterization of the HD RGCs and physiological recordings of their light responses, receptive-field size and structure, and synaptic mechanisms of surround suppression. We also explore the similarities and differences between the HD RGCs and a well characterized RGC with a comparably small receptive field, the local edge detector, in response to moving objects and textures. We model populations of each RGC type to study how they differ in their performance tracking a moving object. These results, besides introducing three new RGC types that together constitute a substantial fraction of mouse RGCs, provide insights into the role of different circuits in shaping RGC receptive fields and establish a foundation for continued study of the mechanisms of surround suppression and the neural basis of motion detection. SIGNIFICANCE STATEMENT: The output cells of the retina, retinal ganglion cells (RGCs), are a diverse group of ∼40 distinct neuron types that are often assigned "feature detection" profiles based on the specific aspects of the visual scene to which they respond. Here we describe, for the first time, morphological and physiological characterization of three new RGC types in the mouse retina, substantially augmenting our understanding of feature selectivity. Experiments and modeling show that while these three "high-definition" RGCs share certain receptive-field properties, they also have distinct tuning to the size, speed, and type of motion on the retina, enabling them to occupy different niches in stimulus space.

Cardinal Orientation Selectivity Is Represented by Two Distinct Ganglion Cell Types in Mouse Retina.

Orientation selectivity (OS) is a prominent and well studied feature of early visual processing in mammals, but recent work has highlighted the possibility that parallel OS circuits might exist in multiple brain locations. Although both classic and modern work has identified an OS mechanism in selective wiring from lateral geniculate nucleus (LGN) to primary visual cortex, OS responses have now been found upstream of cortex in mouse LGN and superior colliculus, suggesting a possible origin in the retina. Indeed, retinal OS responses have been reported for decades in rabbit and more recently in mouse. However, we still know very little about the properties and mechanisms of retinal OS in the mouse, including whether there is a distinct OS ganglion cell type, which orientations are represented, and what are the synaptic mechanisms of retinal OS. We have identified two novel types of OS ganglion cells in the mouse retina that are highly selective for horizontal and vertical cardinal orientations. Reconstructions of the dendritic trees of these OS ganglion cells and measurements of their synaptic conductances offer insights into the mechanism of the OS computation at the earliest stage of the visual system. SIGNIFICANCE STATEMENT: Orientation selectivity (OS) is one of the most well studied computations in the brain and has become a prominent model system in various areas of sensory neuroscience. Although the cortical mechanism of OS suggested by Hubel and Wiesel (1962) has been investigated intensely, other OS cells exist upstream of cortex as early as the retina and the mechanisms of OS in subcortical regions are much less well understood. We identified two ON retinal ganglion cells (RGCs) in mouse that compute OS along the horizontal (nasal-temporal) and vertical (dorsoventral) axes of visual space. We show the relationship between dendritic morphology and OS for each RGC type and reveal new synaptic mechanisms of OS computation in the retina.

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance.

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remains unclear. Here, we systematically identified enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We found that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further showed that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

A presynaptic source drives differing levels of surround suppression in two mouse retinal ganglion cell types.

In early sensory systems, cell-type diversity generally increases from the periphery into the brain, resulting in a greater heterogeneity of responses to the same stimuli. Surround suppression is a canonical visual computation that begins within the retina and is found at varying levels across retinal ganglion cell types. Our results show that heterogeneity in the level of surround suppression occurs subcellularly at bipolar cell synapses. Using single-cell electrophysiology and serial block-face scanning electron microscopy, we show that two retinal ganglion cell types exhibit very different levels of surround suppression even though they receive input from the same bipolar cell types. This divergence of the bipolar cell signal occurs through synapse-specific regulation by amacrine cells at the scale of tens of microns. These findings indicate that each synapse of a single bipolar cell can carry a unique visual signal, expanding the number of possible functional channels at the earliest stages of visual processing.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Conserved variation: identifying patterns of stability and variability in BCR and TCR V genes with different diversity and richness metrics.

The immune system can detect most invading pathogens. The potential for detection of pathogens is dependent on the somatic diversity of the immune repertoires. While it is known that this somatic diversity is carefully generated, it is unclear how the diversity is distributed in the different genes encoding receptors of immune cells. Utilizing different metrics for richness and diversity at the level of small sequence fragments, we present here an analysis of the entire known human germline repertoire as represented by the sequences from the ImMunoGeneTics database of immune receptors. We have developed a fragment sequence quantification analysis to track variation of repertoires with different degrees of precision. Somatic diversity has previously been functionally characterized mostly by division of the V gene sequences into the more conserved and invariant framework (FR) of the receptor and more varied complementarity determining regions (CDR), that interact with the antigen. We find that CDR and FR can be explicitly identified with our sequence fragment diversity quantification technique. In terms of diversity, CDR and FR are especially distinct in B cell V genes. T cell V genes show less of the CDR/FR periodicity but are more diverse overall. Our analysis further shows that there are other areas of diversity outside the CDR and FR that are found widely dispersed in T cell receptor V genes and more tightly focused in FR1 and FR3 in the B cell receptor V genes. The diversity we observe is not dependent on allelic differences nor is this diversity segregated by individual V gene families. We would thus expect that each individual exhibit a diversity equivalent to that of the entire potential repertoire.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Fine spatial information represented in a population of retinal ganglion cells.

Detailed measurement of ganglion cell receptive fields often reveals significant deviations from a smooth, Gaussian profile. We studied the effect of these irregularities on the representation of fine spatial information in the retina. We recorded from nearby clusters of ganglion cells, testing their ability to determine the location of small flashed spots, and we compared the results to the prediction of a Gaussian receptive field model derived from reverse correlation. Despite considerable receptive field overlap, almost all ganglion cell pairs signaled nearly independently. For groups of five cells with highly overlapping receptive fields, the measured light-evoked currents encoded ∼33% more information than predicted by the Gaussian receptive field model. Including measured local irregularities in the receptive field model increased performance to the level observed experimentally. These results suggest that instead of being an unavoidable defect, irregularities may be a positive design feature of population neural codes.

Gap junctions and connexin hemichannels both contribute to the electrical properties of retinal pigment epithelium.

Gap junctions are intercellular channels that permit the transfer of ions and small molecules between adjacent cells. These cellular junctions are particularly dense in the retinal pigment epithelium (RPE), and their contribution to many retinal diseases has been recognized. While gap junctions have been implicated in several aspects of RPE physiology, their role in shaping the electrical properties of these cells has not been characterized in mammals. The role of gap junctions in the electrical properties of the RPE is particularly important considering the growing appreciation of RPE as excitable cells containing various voltage-gated channels. We used a whole-cell patch clamp to measure the electrical characteristics and connectivity between RPE cells, both in cultures derived from human embryonic stem cells and in the intact RPE monolayers from mouse eyes. We found that the pharmacological blockade of gap junctions eliminated electrical coupling between RPE cells, and that the blockade of gap junctions or Cx43 hemichannels significantly increased their input resistance. These results demonstrate that gap junctions function in the RPE not only as a means of molecular transport but also as a regulator of electrical excitability.

Retinal patterns and the cellular repertoire of neuropsin (Opn5) retinal ganglion cells.

Obtaining a parts list of the sensory components of the retina is vital to understand the effects of light in behavior, health, and disease. Rods, cones, and intrinsically photosensitive retinal ganglion cells (ipRGCs) are the best described photoreceptors in the mammalian retina, but recent functional roles have been proposed for retinal neuropsin (Opn5)-an atypical opsin. However, little is known about the pattern of Opn5 expression in the retina. Using cre (Opn5cre ) and cre-dependent reporters, we uncover patterns of Opn5 expression and find that Opn5 is restricted to retinal ganglion cells (RGCs). Opn5-RGCs are nonhomogenously distributed through the retina, with greater densities of cells located in the dorsotemporal quadrant. In addition to the local topology of these cells, using cre-dependent AAV viral tracing, we surveyed their central targets and found that they are biased towards image-forming and image-stabilizing regions. Finally, molecular and electrophysiological profiling reveal that Opn5-RGCs comprise previously defined RGC types that respond optimally to edges and object-motion (F-mini-ONs, HD2, HD1, LEDs, ooDSRGCs, etc.). Together, these data describe the second collection of RGCs that express atypical opsins in the mouse, and expand the roles of image-forming cells in retinal physiology and function.

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

Color vision: More than meets the eye.

Mice can discriminate color, but unlike in primates, studies have so far failed to find robust cone-opponent cells in the retina. A new study shows that a sub-region of the mouse visual thalamus is specialized for processing color.

An offset ON-OFF receptive field is created by gap junctions between distinct types of retinal ganglion cells.

In the vertebrate retina, the location of a neuron's receptive field in visual space closely corresponds to the physical location of synaptic input onto its dendrites, a relationship called the retinotopic map. We report the discovery of a systematic spatial offset between the ON and OFF receptive subfields in F-mini-ON retinal ganglion cells (RGCs). Surprisingly, this property does not come from spatially offset ON and OFF layer dendrites, but instead arises from a network of electrical synapses via gap junctions to RGCs of a different type, the F-mini-OFF. We show that the asymmetric morphology and connectivity of these RGCs can explain their receptive field offset, and we use a multicell model to explore the effects of receptive field offset on the precision of edge-location representation in a population. This RGC network forms a new electrical channel combining the ON and OFF feedforward pathways within the output layer of the retina.

TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes.

Emerging single-cell epigenomic assays are used to investigate the heterogeneity of chromatin activity and its function. However, identifying cells with distinct regulatory elements and clearly visualizing their relationships remains challenging. To this end, we introduce TooManyPeaks to address the need for the simultaneous study of chromatin state heterogeneity in both rare and abundant subpopulations. Our analyses of existing data from three widely used single-cell assays for transposase-accessible chromatin using sequencing (scATAC-seq) show the superior performance of TooManyPeaks in delineating and visualizing pure clusters of rare and abundant subpopulations. Furthermore, the application of TooManyPeaks to new scATAC-seq data from drug-naive and drug-resistant leukemic T cells clearly visualizes relationships among these cells and stratifies a rare "resistant-like" drug-naive sub-clone with distinct cis-regulatory elements.

The vascular landscape of human cancer.

Tumors depend on a blood supply to deliver oxygen and nutrients, making tumor vasculature an attractive anticancer target. However, only a fraction of patients with cancer benefit from angiogenesis inhibitors. Whether antiangiogenic therapy would be more effective if targeted to individuals with specific tumor characteristics is unknown. To better characterize the tumor vascular environment both within and between cancer types, we developed a standardized metric - the endothelial index (EI) - to estimate vascular density in over 10,000 human tumors, corresponding to 31 solid tumor types, from transcriptome data. We then used this index to compare hyper- and hypovascular tumors, enabling the classification of human tumors into 6 vascular microenvironment signatures (VMSs) based on the expression of a panel of 24 vascular "hub" genes. The EI and VMS correlated with known tumor vascular features and were independently associated with prognosis in certain cancer types. Retrospective testing of clinical trial data identified VMS2 classification as a powerful biomarker for response to bevacizumab. Thus, we believe our studies provide an unbiased picture of human tumor vasculature that may enable more precise deployment of antiangiogenesis therapy.