CNS-wide repopulation by hematopoietic-derived microglia-like cells corrects progranulin deficiency in mice.

Hematopoietic stem cell transplantation can deliver therapeutic proteins to the central nervous system (CNS) through transplant-derived microglia-like cells. However, current conditioning approaches result in low and slow engraftment of transplanted cells in the CNS. Here we optimized a brain conditioning regimen that leads to rapid, robust, and persistent microglia replacement without adverse effects on neurobehavior or hematopoiesis. This regimen combines busulfan myeloablation and six days of Colony-stimulating factor 1 receptor inhibitor PLX3397. Single-cell analyses revealed unappreciated heterogeneity of microglia-like cells with most cells expressing genes characteristic of homeostatic microglia, brain-border-associated macrophages, and unique markers. Cytokine analysis in the CNS showed transient inductions of myeloproliferative and chemoattractant cytokines that help repopulate the microglia niche. Bone marrow transplant of progranulin-deficient mice conditioned with busulfan and PLX3397 restored progranulin in the brain and eyes and normalized brain lipofuscin storage, proteostasis, and lipid metabolism. This study advances our understanding of CNS repopulation by hematopoietic-derived cells and demonstrates its therapeutic potential for treating progranulin-dependent neurodegeneration.

MMIL: A novel algorithm for disease associated cell type discovery.

Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple Instance Learning (MMIL), an expectation maximization method that enables the training and calibration of cell-level classifiers using patient-level labels. Our approach can be used to train e.g. lasso logistic regression models, gradient boosted trees, and neural networks. When applied to clinically-annotated, primary patient samples in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL), our method accurately identifies cancer cells, generalizes across tissues and treatment timepoints, and selects biologically relevant features. In addition, MMIL is capable of incorporating cell labels into model training when they are known, providing a powerful framework for leveraging both labeled and unlabeled data simultaneously. Mixture Modeling for MIL offers a novel approach for cell classification, with significant potential to advance disease understanding and management, especially in scenarios with unknown gold-standard labels and high dimensionality.

Genome editing-induced t(4;11) chromosomal translocations model B cell precursor acute lymphoblastic leukemias with KMT2A-AFF1 fusion.

A t(4;11) leukemia model established from CRISPR-engineered chromosomal translocations between the KMT2A and AFF1 genes recapitulate proteomic, epigenomic, and transcriptomic features of primary patient leukemias.

CNS Repopulation by Hematopoietic-Derived Microglia-Like Cells Corrects Progranulin deficiency.

Hematopoietic stem cell transplantation can deliver therapeutic proteins to the CNS through donor-derived hematopoietic cells that become microglia-like cells. However, using standard conditioning approaches, hematopoietic stem cell transplantation is currently limited by low and slow engraftment of microglia-like cells. We report an efficient conditioning regimen based on Busulfan and a six-day course of microglia depletion using the colony-stimulating factor receptor 1 inhibitor PLX3397. Combining Busulfan-myeloablation and transient microglia depletion results in robust, rapid, and persistent microglia replacement by bone marrow-derived microglia-like cells throughout the CNS. Adding PLX3397 does not affect neurobehavior or has adverse effects on hematopoietic reconstitution. Through single-cell RNA sequencing and high-dimensional CyTOF mass cytometry, we show that microglia-like cells are a heterogeneous population and describe six distinct subpopulations. Though most bone-marrow-derived microglia-like cells can be classified as homeostatic microglia, their gene signature is a hybrid of homeostatic/embryonic microglia and border associated-macrophages. Busulfan-myeloablation and transient microglia depletion induce specific cytokines in the brain, ultimately combining myeloid proliferative and chemo-attractive signals that act locally to repopulate microglia from outside the niche. Importantly, this conditioning approach demonstrates therapeutic efficacy in a mouse model of GRN deficiency. Transplanting wild-type bone marrow into Grn-/- mice conditioned with Busulfan plus PLX3397 results in high engraftment of microglia-like cells in the brain and retina, restoring GRN levels and normalizing lipid metabolism.

Dasatinib overcomes glucocorticoid resistance in B-cell acute lymphoblastic leukemia.

Resistance to glucocorticoids (GC) is associated with an increased risk of relapse in B-cell progenitor acute lymphoblastic leukemia (BCP-ALL). Performing transcriptomic and single-cell proteomic studies in healthy B-cell progenitors, we herein identify coordination between the glucocorticoid receptor pathway with B-cell developmental pathways. Healthy pro-B cells most highly express the glucocorticoid receptor, and this developmental expression is conserved in primary BCP-ALL cells from patients at diagnosis and relapse. In-vitro and in vivo glucocorticoid treatment of primary BCP-ALL cells demonstrate that the interplay between B-cell development and the glucocorticoid pathways is crucial for GC resistance in leukemic cells. Gene set enrichment analysis in BCP-ALL cell lines surviving GC treatment show enrichment of B cell receptor signaling pathways. In addition, primary BCP-ALL cells surviving GC treatment in vitro and in vivo demonstrate a late pre-B cell phenotype with activation of PI3K/mTOR and CREB signaling. Dasatinib, a multi-kinase inhibitor, most effectively targets this active signaling in GC-resistant cells, and when combined with glucocorticoids, results in increased cell death in vitro and decreased leukemic burden and prolonged survival in an in vivo xenograft model. Targeting the active signaling through the addition of dasatinib may represent a therapeutic approach to overcome GC resistance in BCP-ALL.

An instructive role for Interleukin-7 receptor α in the development of human B-cell precursor leukemia.

Kinase signaling fuels growth of B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Yet its role in leukemia initiation is unclear and has not been shown in primary human hematopoietic cells. We previously described activating mutations in interleukin-7 receptor alpha (IL7RA) in poor-prognosis "ph-like" BCP-ALL. Here we show that expression of activated mutant IL7RA in human CD34+ hematopoietic stem and progenitor cells induces a preleukemic state in transplanted immunodeficient NOD/LtSz-scid IL2Rγnull mice, characterized by persistence of self-renewing Pro-B cells with non-productive V(D)J gene rearrangements. Preleukemic CD34+CD10highCD19+ cells evolve into BCP-ALL with spontaneously acquired Cyclin Dependent Kinase Inhibitor 2 A (CDKN2A) deletions, as commonly observed in primary human BCP-ALL. CRISPR mediated gene silencing of CDKN2A in primary human CD34+ cells transduced with activated IL7RA results in robust development of BCP-ALLs in-vivo. Thus, we demonstrate that constitutive activation of IL7RA can initiate preleukemia in primary human hematopoietic progenitors and cooperates with CDKN2A silencing in progression into BCP-ALL.

CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.

The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn.

Mass Cytometry of Hematopoietic Cells.

Mass cytometry is now a well-established method that enables the measurement of 40-50 markers (generally proteins but transcripts are also possible) in single cells. Analytes are detected via antibodies tagged with heavy metal and detected by using a time-of-flight mass spectrometer. Over the past decade, mass cytometry has proven to be a valuable method for immunophenotyping hematopoietic cells with remarkable precision in both healthy and malignant scenarios. This chapter explains in detail how to profile hematopoietic cells by using this high-dimensional multiplexed approach.

Single-cell mass cytometry and machine learning predict relapse in childhood leukemia.

Improved insight into cancer cell populations responsible for treatment failure will lead to better outcomes for patients. We herein highlight a single-cell study of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) at diagnosis that revealed hidden developmentally dependent cell signaling states uniquely associated with relapse.

SRC/ABL inhibition disrupts CRLF2-driven signaling to induce cell death in B-cell acute lymphoblastic leukemia.

Children with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) overexpressing the CRLF2 gene (hiCRLF2) have poor prognosis. CRLF2 protein overexpression leads to activated JAK/STAT signaling and trials are underway using JAK inhibitors to overcome treatment failure. Pre-clinical studies indicated limited efficacy of single JAK inhibitors, thus additional pathways must be targeted in hiCRLF2 cells. To identify additional activated networks, we used single-cell mass cytometry to examine 15 BCP-ALL primary patient samples. We uncovered a coordinated signaling network downstream of CRLF2 characterized by co-activation of JAK/STAT, PI3K, and CREB pathways. This CRLF2-driven network could be more effectively disrupted by SRC/ABL inhibition than single-agent JAK or PI3K inhibition, and this could be demonstrated even in primary minimal residual disease (MRD) cells. Our study suggests SCR/ABL inhibition as effective in disrupting the cooperative functional networks present in hiCRLF2 BCP-ALL patients, supporting further investigation of this strategy in pre-clinical studies.

Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse.

Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.