Biology subjects

Arai, F.

Publications and source records attributed to Arai, F..

8 recordsLinked to original sources

bioRxiv2026

Highly plastic macrophage niches orchestrate acquired quiescence and reactivation in breast-cancer bone metastasis

Recurrence and metastasis remain major causes of cancer mortality, sustained by therapy-resistant micrometastatic cells. Bone is a frequent site of breast-cancer relapse, yet the cues that reawaken disseminated cells remain poorly defined. We identify a previously unrecognized, highly plastic CXCL16 macrophage population that integrates tumor-associated macrophage programs found in distant metastatic sites such as lung and brain with non-tumor disease-associated traits in bone marrow. These CXCL16 macrophages establish a transient niche that restrains disseminated cancer-cell proliferation. Single-cell transcriptomics delineate functional remodeling of myeloid niches within the bone metastatic microenvironment: a CXCL16 macrophage niche that transiently constrains metastatic growth, and G-CSF macrophage and neutrophil niches that reignite tumor outgrowth. In primary tumors, cancer-associated fibroblasts (CAFs) aberrantly secrete G-CSF in response to cancer-cell signals, expanding G-CSF-receptor-positive subset of cancer cells with high metastatic potential. In advanced human bone metastases, CXCL16 macrophages localize to CAF-rich stroma but are excluded from cancer-cell clusters, indicating immune evasion. Together, these findings uncover CAF-bone-marrow cross-talk as a therapeutic target linking stromal inflammation, immune remodeling, and metastatic progression.

cancer biology↗

bioRxiv2024

Flow zoometry of Drosophila

Drosophila serves as a highly valuable model organism across numerous fields including genetics, immunology, neuroscience, cancer biology, and developmental biology. Central to Drosophila-based biological research is the ability to perform comprehensive genetic or chemical screens. However, this research is often limited by its dependence on laborious manual handling and analysis, making it prone to human error and difficult to discern statistically significant or rare events amid the noise of individual variations resulting from genetic and environmental factors. In this article we present flow zoometry, a whole-animal equivalent of flow cytometry for large-scale, individual-level, high-content screening of Drosophila. Our flow zoometer automatically clears the tissues of Drosophila melanogaster, captures three-dimensional (3D) multi-color fluorescence tomograms of single flies with single-cell volumetric resolution at an unprecedented throughput of over 1,000 animals within 48 hours (24 hr for clearing; 24 hr for imaging), and performs AI-enhanced data-driven analysis - a task that would traditionally take months or years with manual techniques. To demonstrate its broad applications, we employed the flow zoometer in various laborious screening assays, including those in toxicology, genotyping, and tumor screening. Flow zoometry represents a pivotal evolution in high-throughput screening technology: previously from molecules to cells, now from cells to whole animals. This advancement serves as a foundational platform for "statistical spatial biology", to improve empirical precision and enable serendipitous discoveries across various fields of biology.

bioengineering↗

bioRxiv2022

Polycomb repressive complex 1.1 coordinates homeostatic and emergency myelopoiesis

Polycomb repressive complex (PRC) 1 regulates stem cell fate by mediating mono-ubiquitination of histone H2A at lysine 119. While canonical PRC1 is critical for hematopoietic stem and progenitor cell (HSPC) maintenance, the role of non-canonical PRC1 in hematopoiesis remains elusive. PRC1.1, a non-canonical PRC1, consists of PCGF1, RING1B, KDM2B, and BCOR. We recently showed that PRC1.1 insufficiency induced by the loss of PCGF1 or BCOR causes myeloid-biased hematopoiesis and promotes transformation of hematopoietic cells in mice. Here we show that PRC1.1 serves as an epigenetic switch that coordinates homeostatic and emergency hematopoiesis. PRC1.1 maintains balanced output of steady-state hematopoiesis by restricting C/EBP-dependent precocious myeloid differentiation of HSPCs and the HOXA9- and {beta}-catenin-driven self-renewing network in myeloid progenitors. Upon regeneration, PRC1.1 is transiently inhibited to facilitate formation of granulocyte-macrophage progenitor (GMP) clusters, thereby promoting emergency myelopoiesis. Moreover, constitutive inactivation of PRC1.1 results in unchecked expansion of GMPs and eventual transformation. Collectively, our results define PRC1.1 as a novel critical regulator of emergency myelopoiesis, dysregulation of which leads to myeloid transformation.

immunology↗

bioRxiv2020

Heterogeneity and 'memory in stem cell populations

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally pure cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories - the ability of individual cells to record their developmental past and adapt their response to their environment accordingly - are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation. Using this model we argue that heterogeneity is central to stem cell population function, and memories naturally explain why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

cell biology↗

bioRxiv2019

Mapping biology from mouse to man using transfer learning

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. 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 biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.

systems biology↗

bioRxiv2018

Cell division history determines hematopoietic stem cell potency

Loss of stem cell self-renewal may underpin aging. Here, we combined single cell profiling, deep-learning, mathematical modelling and in vivo functional studies to explore how hematopoietic stem cell (HSC) division patterns evolve with age. We trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from their gene expression patterns. We then used this ANN to compare daughter cell identities immediately after HSC divisions, and found that HSC self-renewal declines sharply with age. Furthermore, while HSC cell divisions are deterministic and intrinsically-regulated in young and old age, they become stochastic and niche-sensitive in mid-life. Analysis of evolving division patterns indicated that the self-renewal ability an individual HSC depends upon number times it has divided previously. We propose a model of HSC proliferation that accurately predicts the accumulation of HSCs with age, and reconciles the stochastic and instructive views of fate commitment.

cell biology↗

bioRxiv2018

Environmental Optimization Enables Maintenance of Quiescent Hematopoietic Stem Cells Ex Vivo

Hematopoietic stem cells (HSCs) maintain lifelong hematopoiesis by remaining quiescent in the bone marrow niche. Recapitulation of a quiescent state in culture has not been achieved, as cells rapidly proliferate and differentiate in vitro. After exhaustive analysis of different environmental factor combinations and concentrations as a way to mimic physiological conditions, we were able to maintain engraftable quiescent HSCs for 1 month in culture under very low cytokine concentrations, hypoxia, and very high fatty acid levels. Exogenous fatty acids were required likely due to suppression of intrinsic fatty acid synthesis by hypoxia and low cytokine conditions. By contrast, high cytokine concentrations or normoxia induced HSC proliferation and differentiation. Our novel culture system provides a means to evaluate properties of steady state HSCs and test effects of defined factors in vitro under near-physiological conditions.

cell biology↗

bioRxiv2017

Stem cell differentiation is a stochastic process with memory

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.

systems biology↗