A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Integrated multimodal cell atlas of Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Temporal analysis of cell-type proportions indicated an early reduction of Somatostatin-expressing neuronal subtypes and a late decrease of supragranular intratelencephalic-projecting excitatory and Parvalbumin-expressing neurons, with increases in disease-associated microglial and astrocytic states. We found complex gene expression differences, ranging from global to cell type-specific effects. These effects showed different temporal patterns indicating diverse cellular perturbations as a function of disease progression. A subset of donors showed a particularly severe cellular and molecular phenotype, which correlated with steeper cognitive decline. We have created a freely available public resource to explore these data and to accelerate progress in AD research at SEA-AD.org.

Regenerative lineages and immune-mediated pruning in lung cancer metastasis.

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types, typically seen in response to lung injury, and by striking infidelity among transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9, and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9-dependent resistance to natural killer cells. Loss of developmental stage-specific constraint in macrometastases triggered by natural killer cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation.

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data.

We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Scalable microfluidics for single-cell RNA printing and sequencing.

Many important biological questions demand single-cell transcriptomics on a large scale. Hence, new tools are urgently needed for efficient, inexpensive manipulation of RNA from individual cells. We report a simple platform for trapping single-cell lysates in sealed, picoliter microwells capable of printing RNA on glass or capturing RNA on beads. We then develop a scalable technology for genome-wide, single-cell RNA-Seq. Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments. We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

Broadening horizons: holistic viewpoints from the Biology of Genomes.

A report on the Cold Spring Harbor Laboratory 27th annual meeting on the Biology of Genomes, held in Cold Spring Harbor, New York, USA, 6-10 May 2014.

Predictors of ethanol consumption in adult Sprague-Dawley rats: relation to hypothalamic peptides that stimulate ethanol intake.

To investigate mechanisms in outbred animals that increase the propensity to consume ethanol, it is important to identify and characterize these animals before or at early stages in their exposure to ethanol. In the present study, different measures were examined in adult Sprague-Dawley rats to determine whether they can predict long-term propensity to overconsume ethanol. Before consuming 9% ethanol with a two-bottle choice paradigm, rats were examined with the commonly used behavioral measures of novelty-induced locomotor activity and anxiety, as assessed during 15 min in an open-field activity chamber. Two additional measures, intake of a low 2% ethanol concentration or circulating triglyceride (TG) levels after a meal, were also examined with respect to their ability to predict chronic 9% ethanol consumption. The results revealed significant positive correlations across individual rats between the amount of 9% ethanol ultimately consumed and three of these different measures, with high scores for activity, 2% ethanol intake, and TGs identifying rats that consume 150% more ethanol than rats with low scores. Measurements of hypothalamic peptides that stimulate ethanol intake suggest that they contribute early to the greater ethanol consumption predicted by these high scores. Rats with high 2% ethanol intake or high TGs, two measures found to be closely related, had significantly elevated expression of enkephalin (ENK) and galanin (GAL) in the hypothalamic paraventricular nucleus (PVN) but no change in neuropeptide Y (NPY) in the arcuate nucleus (ARC). This is in contrast to rats with high activity scores, which in addition to elevated PVN ENK expression showed enhanced NPY in the ARC but no change in GAL. Elevated ENK is a common characteristic related to all three predictors of chronic ethanol intake, whereas the other peptides differentiate these predictors, with GAL enhanced with high 2% ethanol intake and TG measures but NPY related to activity.

Opioids in the hypothalamic paraventricular nucleus stimulate ethanol intake.

BACKGROUND: Specialized hypothalamic systems that increase food intake might also increase ethanol intake. To test this possibility, morphine and receptor-specific opioid agonists were microinjected in the paraventricular nucleus (PVN) of rats that had learned to drink ethanol. To cross-validate the results, naloxone methiodide (m-naloxone), an opioid antagonist, was microinjected with the expectation that it would have the opposite effect of morphine and the specific opioid agonists. METHODS: Sprague-Dawley rats were trained, without sugar, to drink 4 or 7% ethanol and were then implanted with chronic brain cannulas aimed at the PVN. After recovery, those drinking 7% ethanol, with food and water available, were injected with 2 doses each of morphine or m-naloxone. To test for receptor specificity, 2 doses each of the mu-receptor agonist [D-Ala(2),N-Me-Phe(4),Gly(5)-ol]-Enkephalin (DAMGO), delta-receptor agonist D-Ala-Gly-Phe-Met-NH2 (DALA), or kappa-receptor agonist U-50,488H were injected. DAMGO was also tested in rats drinking 4% ethanol without food or water available. As an anatomical control for drug reflux, injections were made 2 mm dorsal to the PVN. RESULTS: A main result was a significant increase in ethanol intake induced by PVN injection of morphine. The opposite effect was produced by m-naloxone. The effects of morphine and m-naloxone were exclusively on intake of ethanol, even though food and water were freely available. In the analysis with specific receptor agonists, PVN injection of the delta-agonist DALA significantly increased 7% ethanol intake without affecting food or water intake. This is in contrast to the kappa-agonist U-50,488H, which decreased ethanol intake, and the mu-agonist DAMGO, which had no effect on ethanol intake in the presence or absence of food and water. In the anatomical control location 2 mm dorsal to the PVN, no drug caused any significant changes in ethanol, food, or water intake, providing evidence that the active site was close to the cannula tip. CONCLUSIONS: The delta-opioid receptor agonist in the PVN increased ethanol intake in strong preference over food and water, while the kappa-opioid agonist suppressed ethanol intake. Prior studies show that learning to drink ethanol stimulates PVN expression and production of the peptides enkephalin and dynorphin, which are endogenous agonists for the delta- and kappa-receptors, respectively. These results suggest that enkephalin via the delta-opioid system can function locally within a positive feedback circuit to cause ethanol intake to escalate and ultimately contribute to the abuse of ethanol. This is in contrast to dynorphin via the kappa-opioid system, which may act to counter this escalation. Naltrexone therapy for alcoholism may act, in part, by blocking the enkephalin-triggered positive feedback cycle.

Opioids in the nucleus accumbens stimulate ethanol intake.

The nucleus accumbens (NAc) participates in the control of both motivation and addiction. To test the possibility that opioids in the NAc can cause rats to select ethanol in preference to food, Sprague-Dawley rats with ethanol, food, and water available, were injected with two doses each of morphine, the mu-receptor agonist [D-Ala(2),N-Me-Phe(4),Gly(5)-ol]-Enkephalin (DAMGO), the delta-receptor agonist D-Ala-Gly-Phe-Met-NH2 (DALA), the k-receptor agonist (+/-)-trans-U-50488 methanesulfonate (U-50,488H), or the opioid antagonist naloxone methiodide (m-naloxone). As an anatomical control for drug reflux, injections were also made 2mm above the NAc. The main result was that morphine in the NAc significantly increased ethanol and food intake, whereas m-naloxone reduced ethanol intake without affecting food or water intake. Of the selective receptor agonists, DALA in the NAc increased ethanol intake in preference to food. This is in contrast to DAMGO, which stimulated food but not ethanol intake, and the k-agonist U-50,488H, which had no effect on intake. When injected in the anatomical control site 2mm dorsal to the NAc, the opioids had no effects on ethanol intake. These results demonstrate that ethanol intake produced by morphine in the NAc is driven in large part by the delta-receptor. In light of other studies showing ethanol intake to increase enkephalin expression in the NAc, the present finding of enkephalin-induced ethanol intake suggests the existence of a positive feedback loop that fosters alcohol abuse. Naltrexone therapy for alcohol abuse may then act, in part, in the NAc by blocking this opioid-triggered cycle of alcohol intake.