An age-progressive platelet differentiation path from hematopoietic stem cells causes exacerbated thrombosis.

Platelet dysregulation is drastically increased with advanced age and contributes to making cardiovascular disorders the leading cause of death of elderly humans. Here, we reveal a direct differentiation pathway from hematopoietic stem cells into platelets that is progressively propagated upon aging. Remarkably, the aging-enriched platelet path is decoupled from all other hematopoietic lineages, including erythropoiesis, and operates as an additional layer in parallel with canonical platelet production. This results in two molecularly and functionally distinct populations of megakaryocyte progenitors. The age-induced megakaryocyte progenitors have a profoundly enhanced capacity to engraft, expand, restore, and reconstitute platelets in situ and upon transplantation and produce an additional platelet population in old mice. The two pools of co-existing platelets cause age-related thrombocytosis and dramatically increased thrombosis in vivo. Strikingly, aging-enriched platelets are functionally hyper-reactive compared with the canonical platelet populations. These findings reveal stem cell-based aging as a mechanism for platelet dysregulation and age-induced thrombosis.

CAR T Cell Therapy Progress and Challenges for Solid Tumors.

The past two decades have marked the beginning of an unprecedented success story for cancer therapy through redirecting antitumor immunity [1]. While the mechanisms that control the initial and ongoing immune responses against tumors remain a strong research focus, the clinical development of technologies that engage the immune system to target and kill cancer cells has become a translational research priority. Early attempts documented in the late 1800s aimed at sparking immunity with cancer vaccines were difficult to interpret but demonstrated an opportunity that more than 100 years later has blossomed into the current field of cancer immunotherapy. Perhaps the most recent and greatest illustration of this is the widespread appreciation that tumors actively shut down antitumor immunity, which has led to the emergence of checkpoint pathway inhibitors that re-invigorate the body's own immune system to target cancer [2, 3]. This class of drugs, with first FDA approvals in 2011, has demonstrated impressive durable clinical responses in several cancer types, including melanoma, lung cancer, Hodgkin's lymphoma, and renal cell carcinoma, with the ongoing investigation in others. The biology and ultimate therapeutic successes of these drugs led to the 2018 Nobel Prize in Physiology or Medicine, awarded to Dr. James Allison and Dr. Tasuku Honjo for their contributions to cancer therapy [4]. In parallel to the emerging science that aided in unleashing the body's own antitumor immunity with checkpoint pathway inhibitors, researchers were also identifying ways to re-engineer antitumor immunity through adoptive cellular immunotherapy approaches. Chimeric antigen receptor (CAR)-based T cell therapy has achieved an early head start in the field, with two recent FDA approvals in 2017 for the treatment of B-cell malignancies [5]. There is an explosion of preclinical and clinical efforts to expand the therapeutic indications for CAR T cell therapies, with a specific focus on improving their clinical utility, particularly for the treatment of solid tumors. In this chapter, we will highlight the recent progress, challenges, and future perspectives surrounding the development of CAR T cell therapies for solid tumors.

Oncomir miR-125b regulates hematopoiesis by targeting the gene Lin28A.

MicroRNA-125b (miR-125b) is up-regulated in patients with leukemia. Overexpression of miR-125b alone in mice causes a very aggressive, transplantable myeloid leukemia. Before leukemia, these mice do not display elevation of white blood cells in the spleen or bone marrow; rather, the hematopoietic compartment shows lineage-skewing, with myeloid cell numbers dramatically increased and B-cell numbers severely diminished. miR-125b exerts this effect by up-regulating the number of common myeloid progenitors while inhibiting development of pre-B cells. We applied a miR-125b sponge loss of function system in vivo to show that miR-125b physiologically regulates hematopoietic development. Investigating the mechanism by which miR-125b regulates hematopoiesis, we found that, among a panel of candidate targets, the mRNA for Lin28A, an induced pluripotent stem cell gene, was most repressed by miR-125b in mouse hematopoietic stem and progenitor cells. Overexpressing Lin28A in the mouse hematopoietic system mimicked the phenotype observed on inhibiting miR-125b function, leading to a decrease in hematopoietic output. Relevant to the miR-125b overexpression phenotype, we also found that knockdown of Lin28A led to hematopoietic lineage-skewing, with increased myeloid and decreased B-cell numbers. Thus, the miR-125b target Lin28A is an important regulator of hematopoiesis and a primary target of miR-125b in the hematopoietic system.

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Pregnancy Reduces Il33+ Hybrid Progenitor Accumulation in the Aged Mammary Gland.

Aging increases breast cancer risk while an early first pregnancy reduces a woman's life-long risk. Several studies have explored the effect of either aging or pregnancy on mammary epithelial cells (MECs), but the combined effect of both remains unclear. Here, we interrogate the functional and transcriptomic changes at single cell resolution in the mammary gland of aged nulliparous and parous mice to discover that pregnancy normalizes age-related imbalances in lineage composition, while also inducing a differentiated cell state. Importantly, we uncover a minority population of Il33-expressing hybrid MECs with high cellular potency that accumulate in aged nulliparous mice but is significantly reduced in aged parous mice. Functionally, IL33 treatment of basal, but not luminal, epithelial cells from young mice phenocopies aged nulliparous MECs and promotes formation of organoids with Trp53 knockdown. Collectively, our study demonstrates that pregnancy blocks the age-associated loss of lineage integrity in the basal layer through a decrease in Il33+ hybrid MECs, potentially contributing to pregnancy-induced breast cancer protection.

Ten challenges and opportunities in computational immuno-oncology.

Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation.

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

PD-L1 blockade restores CAR T cell activity through IFN-γ-regulation of CD163+ M2 macrophages.

BACKGROUND: The immune suppressive tumor microenvironment (TME) that inhibits T cell infiltration, survival, and antitumor activity has posed a major challenge for developing effective immunotherapies for solid tumors. Chimeric antigen receptor (CAR)-engineered T cell therapy has shown unprecedented clinical response in treating patients with hematological malignancies, and intense investigation is underway to achieve similar responses with solid tumors. Immunologically cold tumors, including prostate cancers, are often infiltrated with abundant tumor-associated macrophages (TAMs), and infiltration of CD163+ M2 macrophages correlates with tumor progression and poor responses to immunotherapy. However, the impact of TAMs on CAR T cell activity alone and in combination with TME immunomodulators is unclear. METHODS: To model this in vitro, we utilized a novel co-culture system with tumor cells, CAR T cells, and polarized M1 or M2 macrophages from CD14+ peripheral blood mononuclear cells collected from healthy human donors. Tumor cell killing, T cell activation and proliferation, and macrophage phenotypes were evaluated by flow cytometry, cytokine production, RNA sequencing, and functional blockade of signaling pathways using antibodies and small molecule inhibitors. We also evaluated the TME in humanized mice following CAR T cell therapy for validation of our in vitro findings. RESULTS: We observed inhibition of CAR T cell activity with the presence of M2 macrophages, but not M1 macrophages, coinciding with a robust induction of programmed death ligand-1 (PD-L1) in M2 macrophages. We observed similar PD-L1 expression in TAMs following CAR T cell therapy in the TME of humanized mice. PD-L1, but not programmed cell death protein-1, blockade in combination with CAR T cell therapy altered phenotypes to more M1-like subsets and led to loss of CD163+ M2 macrophages via interferon-γ signaling, resulting in improved antitumor activity of CAR T cells. CONCLUSION: This study reveals an alternative mechanism by which the combination of CAR T cells and immune checkpoint blockade modulates the immune landscape of solid tumors to enhance therapeutic efficacy of CAR T cells.

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution.

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.