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Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans
Get PDFNervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes
Co-inhibition of SMAD and MAPK signaling enhances 124I uptake in BRAF-mutant thyroid cancers.
Get PDFConstitutive MAPK activation silences genes required for iodide uptake and thyroid hormone biosynthesis in thyroid follicular cells. Accordingly, most BRAFV600E papillary thyroid cancers (PTC) are refractory to radioiodide (RAI) therapy. MAPK pathway inhibitors rescue thyroid-differentiated properties and RAI responsiveness in mice and patient subsets with BRAFV600E-mutant PTC. TGFB1 also impairs thyroid differentiation and has been proposed to mediate the effects of mutant BRAF. We generated a mouse model of BRAFV600E-PTC with thyroid-specific knockout of the Tgfbr1 gene to investigate the role of TGFB1 on thyroid-differentiated gene expression and RAI uptake in vivo. Despite appropriate loss of Tgfbr1, pSMAD levels remained high, indicating that ligands other than TGFB1 were engaging in this pathway. The activin ligand subunits Inhba and Inhbb were found to be overexpressed in BRAFV600E-mutant thyroid cancers. Treatment with follistatin, a potent inhibitor of activin, or vactosertib, which inhibits both TGFBR1 and the activin type I receptor ALK4, induced a profound inhibition of pSMAD in BRAFV600E-PTCs. Blocking SMAD signaling alone was insufficient to enhance iodide uptake in the setting of constitutive MAPK activation. However, combination treatment with either follistatin or vactosertib and the MEK inhibitor CKI increased 124I uptake compared to CKI alone. In summary, activin family ligands converge to induce pSMAD in Braf-mutant PTCs. Dedifferentiation of BRAFV600E-PTCs cannot be ascribed primarily to activation of SMAD. However, targeting TGFβ/activin-induced pSMAD augmented MAPK inhibitor effects on iodine incorporation into BRAF tumor cells, indicating that these two pathways exert interdependent effects on the differentiation state of thyroid cancer cells
Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome.
Get PDFWhile tumor infiltration by CD8+ T cells is now widely accepted to predict outcomes, the clinical significance of intratumoral B cells is less clear. We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques (fractal dimension differences and a box-counting method 'occupancy') to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors
Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
Get PDFHigh-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain
De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.
No full textWe report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation
Comparative cellular analysis of motor cortex in human, marmoset and mouse
Get PDFThe primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations
Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories
Get PDFMany complex systems operating far from the equilibrium exhibit stochastic dynamics that can be described by a Langevin equation. Inferring Langevin equations from data can reveal how transient dynamics of such systems give rise to their function. However, dynamics are often inaccessible directly and can be only gleaned through a stochastic observation process, which makes the inference challenging. Here we present a non-parametric framework for inferring the Langevin equation, which explicitly models the stochastic observation process and non-stationary latent dynamics. The framework accounts for the non-equilibrium initial and final states of the observed system and for the possibility that the system's dynamics define the duration of observations. Omitting any of these non-stationary components results in incorrect inference, in which erroneous features arise in the dynamics due to non-stationary data distribution. We illustrate the framework using models of neural dynamics underlying decision making in the brain
PQN-59 and GTBP-1 contribute to stress granule formation but are not essential for their assembly in C. elegans embryos
Get PDFWhen exposed to stressful conditions, eukaryotic cells respond by inducing the formation of cytoplasmic ribonucleoprotein complexes called stress granules. Here we use C. elegans to study two proteins that are important for stress granule assembly in human cells: PQN-59, the human UBAP2L ortholog, and GTBP-1, the human G3BP1/2 ortholog. Both proteins assemble into stress granules in the embryo and in the germline when C. elegans is exposed to stressful conditions. None of the two proteins is essential for the assembly of stress-induced granules, as shown by the single and combined depletions by RNAi, and neither pqn-59 nor gtbp-1 mutant embryos show higher sensitivity to stress than control embryos. We find that pqn-59 mutants display reduced progeny and a high percentage of embryonic lethality, phenotypes that are not dependent on stress exposure and that are not shared with gtbp-1 mutants. Our data indicate that, in contrast to human cells, PQN-59 and GTBP-1 are not required for stress granule formation but that PQN-59 is important for C. elegans development