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Sound source localization is critical to animal survival and for identification of auditory objects. We investigated the acuity with which humans localize low frequency, pure tone sounds using timing differences between the ears. These small differences in time, known as interaural time differences or ITDs, are identified in a manner that allows localization acuity of around 1° at the midline. Acuity, a relative measure of localization ability, displays a non-linear variation as sound sources are positioned more laterally. All species studied localize sounds best at the midline and progressively worse as the sound is located out towards the side. To understand why sound localization displays this variation with azimuthal angle, we took a first-principles, systemic, analytical approach to model localization acuity. We calculated how ITDs vary with sound frequency, head size and sound source location for humans. This allowed us to model ITD variation for previously published experimental acuity data and determine the distribution of just-noticeable differences in ITD. Our results suggest that the best-fit model is one whereby just-noticeable differences in ITDs are identified with uniform or close to uniform sensitivity across the physiological range. We discuss how our results have several implications for neural ITD processing in different species as well as development of the auditory system.

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research. Patrick Stumpf et al. use a machine learning technique called transfer learning to compare bone marrow cell-type information between mice and humans, based on single-cell RNA-seq data. Using their model, they identify aspects of cellular expression profiles that transfer and those that don’t, which can be used to understand when mouse models of human disease are appropriate.

Background Hypoxia is a hallmark of the tumor microenvironment (TME) and in addition to altering metabolism in cancer cells, it transforms tumor-associated stromal cells. Within the tumor stromal cell compartment, tumor-associated macrophages (TAMs) provide potent pro-tumoral support. However, TAMs can also be harnessed to destroy tumor cells by monoclonal antibody (mAb) immunotherapy, through antibody dependent cellular phagocytosis (ADCP). This is mediated via antibody-binding activating Fc gamma receptors (FcγR) and impaired by the single inhibitory FcγR, FcγRIIb. Methods We applied a multi-OMIC approach coupled with in vitro functional assays and murine tumor models to assess the effects of hypoxia inducible factor (HIF) activation on mAb mediated depletion of human and murine cancer cells. For mechanistic assessments, siRNA-mediated gene silencing, Western blotting and chromatin immune precipitation were utilized to assess the impact of identified regulators on FCGR2B gene transcription. Results We report that TAMs are FcγRIIb bright relative to healthy tissue counterparts and under hypoxic conditions , mononuclear phagocytes markedly upregulate FcγRIIb. This enhanced FcγRIIb expression is transcriptionally driven through HIFs and Activator protein 1 (AP-1). Importantly, this phenotype reduces the ability of macrophages to eliminate anti-CD20 monoclonal antibody (mAb) opsonized human chronic lymphocytic leukemia cells in vitro and EL4 lymphoma cells in vivo in human FcγRIIb + / + transgenic mice. Furthermore, post-HIF activation, mAb mediated blockade of FcγRIIb can partially restore phagocytic function in human monocytes. Conclusion Our findings provide a detailed molecular and cellular basis for hypoxia driven resistance to antitumor mAb immunotherapy, unveiling a hitherto unexplored aspect of the TME. These findings provide a mechanistic rationale for the modulation of FcγRIIb expression or its blockade as a promising strategy to enhance approved and novel mAb immunotherapies.

Background Hypoxia is a hallmark of the tumor microenvironment (TME) and in addition to altering metabolism in cancer cells, it transforms tumor-associated stromal cells. Within the tumor stromal cell compartment, tumor-associated macrophages (TAMs) provide potent pro-tumoral support. However, TAMs can also be harnessed to destroy tumor cells by monoclonal antibody (mAb) immunotherapy, through antibody dependent cellular phagocytosis (ADCP). This is mediated via antibody-binding activating Fc gamma receptors (Fc[gamma]R) and impaired by the single inhibitory Fc[gamma]R, Fc[gamma]RIIb. Methods We applied a multi-OMIC approach coupled with in vitro functional assays and murine tumor models to assess the effects of hypoxia inducible factor (HIF) activation on mAb mediated depletion of human and murine cancer cells. For mechanistic assessments, siRNA-mediated gene silencing, Western blotting and chromatin immune precipitation were utilized to assess the impact of identified regulators on FCGR2B gene transcription. Results We report that TAMs are Fc[gamma]RIIb.sup.bright relative to healthy tissue counterparts and under hypoxic conditions, mononuclear phagocytes markedly upregulate Fc[gamma]RIIb. This enhanced Fc[gamma]RIIb expression is transcriptionally driven through HIFs and Activator protein 1 (AP-1). Importantly, this phenotype reduces the ability of macrophages to eliminate anti-CD20 monoclonal antibody (mAb) opsonized human chronic lymphocytic leukemia cells in vitro and EL4 lymphoma cells in vivo in human Fc[gamma]RIIb.sup.+/+ transgenic mice. Furthermore, post-HIF activation, mAb mediated blockade of Fc[gamma]RIIb can partially restore phagocytic function in human monocytes. Conclusion Our findings provide a detailed molecular and cellular basis for hypoxia driven resistance to antitumor mAb immunotherapy, unveiling a hitherto unexplored aspect of the TME. These findings provide a mechanistic rationale for the modulation of Fc[gamma]RIIb expression or its blockade as a promising strategy to enhance approved and novel mAb immunotherapies. Keywords: Hypoxia, Hypoxia inducible factors, Fc[gamma]RIIb, Fc gamma receptors, Tumor-associated macrophages, Monocytes, Monoclonal antibody, Tumor microenvironment, Resistance, Cancer

Purpose of Review The purpose of this study is to outline how ideas from information theory may be used to analyze single-cell data and better understand stem cell behavior. Recent Findings Recent technological breakthroughs in single-cell profiling have made it possible to interrogate cell–cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional, and noisy datasets that arise from single-cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability, and dynamics. Summary We provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single-cell level. We also discuss how work in this area might develop in the near future.

Purpose of Review: To outline how ideas from Information Theory may be used to analyze single cell data and better understand stem cell behaviour. Recent findings: Recent technological breakthroughs in single cell profiling have made it possible to interrogate cell-to-cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional and noisy datasets that arise from single cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability and dynamics. Summary: We provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single cell level. We also discuss how work in this area might develop in the near future.

Pluripotent stem cells are able to self-renew indefinitely in culture and differentiate into all somatic cell types in vivo. While much is known about the molecular basis of pluripotency, the molecular mechanisms of lineage commitment are complex and only partially understood. Here, using a combination of single cell profiling and mathematical modeling, we examine the differentiation dynamics of individual mouse embryonic stem cells (ESCs) as they progress from the ground state of pluripotency along the neuronal lineage. In accordance with previous reports we find that cells do not transit directly from the pluripotent state to the neuronal state, but rather first stochastically permeate an intermediate primed pluripotent state, similar to that found in the maturing epiblast in development. However, analysis of rate at which individual cells enter and exit this intermediate metastable state using a hidden Markov model reveals that the observed ESC and epiblast-like 'macrostates' conceal a chain of unobserved cellular 'microstates', which individual cells transit through stochastically in sequence. These hidden microstates ensure that individual cells spend well-defined periods of time in each functional macrostate and encode a simple form of epigenetic 'memory' that allows individual cells to record their position on the differentiation trajectory. To examine the generality of this model we also consider the differentiation of mouse hematopoietic stem cells along the myeloid lineage and observe remarkably similar dynamics, suggesting a general underlying process. Based upon these results we suggest a statistical mechanics view of cellular identities that distinguishes between functionally-distinct macrostates and the many functionally-similar molecular microstates associated with each macrostate. Taken together these results indicate that differentiation is a discrete stochastic process amenable to analysis using the tools of statistical mechanics.

The avian auditory brainstem contains specialized nuclei critical for sound localization, including the nucleus laminaris (nL), which forms as a single-cell-thick lamina essential for computing interaural time differences. Despite its functional importance, the molecular mechanisms guiding nL lamina formation have remained poorly understood. Here, we identify a signalling cascade involving FGF8, MafB, and cadherin-22 that orchestrates this morphogenetic process. We show that FGF8 is selectively expressed in the developing auditory hindbrain and correlates spatiotemporally with lamina formation in the nL. Disruption of FGF signalling—either via misexpression of FGF8 or dominant-negative FGFR1—perturbs the formation of the nL and alters cadherin-22 expression. In vitro culture experiments further reveal that nL lamination is sensitive to FGF8 dosage, with an optimal concentration required for both FGF8 and MafB expression and correct structural organization. We demonstrate that FGF8 induces MafB, which in turn regulates cadherin-22 expression, a cell adhesion molecule enriched in the dendrites of nL neurons. Functional disruption of cadherins impairs lamina formation and leads to scattered FGF8 expression, indicating a feedback loop between adhesion and signalling. Computational models—both static and dynamic—show that bipolar dendrite-localized adhesion can drive laminar architecture as the maximum adhesion configuration. These findings establish a novel molecular and biophysical mechanism for neuronal lamination in the vertebrate hindbrain, showing how local FGF signalling, transcriptional regulation, and dendritic adhesion converge to shape neural circuitry essential for sound localization.