Nociplastic Pain in Endometriosis: A Scoping Review.

Endometriosis is an inflammatory chronic condition associated with nociceptive, neuropathic, and nociplastic pain. Central sensitization (CS) is the primary nociplastic pain mechanism. However, there are currently no standardized methods for detecting CS or nociplastic pain.

A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis.

Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties.

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML.

T cells are important for the control of acute myeloid leukemia (AML), a common and often deadly malignancy. We observed that some AML patient samples are resistant to killing by human-engineered cytotoxic CD4 T cells. Single-cell RNA-seq of primary AML samples and CD4 T cells before and after their interaction uncovered transcriptional programs that correlate with AML sensitivity or resistance to CD4 T cell killing.

Endometrioid Endometrial RNA Index Predicts Recurrence in Stage I Patients.

Purpose: Risk prediction with genomic and transcriptomic data has the potential to improve patient outcomes by enabling clinicians to identify patients requiring adjuvant treatment approaches, while sparing low-risk patients from unnecessary interventions. Endometrioid endometrial carcinoma (EEC) is the most common cancer in women in developed countries, and rates of endometrial cancer are increasing.

Experimental Design: We collected a 105-patient case-control cohort of stage I EEC comprising 45 patients who experienced recurrence less than 6 years after excision, and 60 Fédération Internationale de Gynécologie et d'Obstétrique grade-matched controls without recurrence.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes.

Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer.

Background: The prognosis for patients with head and neck cancer (HNC) is poor and has improved little in recent decades, partially due to lack of therapeutic options. To identify effective therapeutic targets, we sought to identify molecular pathways that drive metastasis and HNC progression, through large-scale systematic analyses of transcriptomic data.

Methods: We performed meta-analysis across 29 gene expression studies including 2074 primary HNC biopsies to identify genes and transcriptional pathways associated with survival and lymph node metastasis (LNM).

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML.

Unlabelled: T cells are important for the control of acute myeloid leukemia (AML), a common and often deadly malignancy. We observed that some AML patient samples are resistant to killing by human engineered cytotoxic CD4 T cells. Single-cell RNA-seq of primary AML samples and CD4 T cells before and after their interaction uncovered transcriptional programs that correlate with AML sensitivity or resistance to CD4 T cell killing.

Multimodal data fusion for cancer biomarker discovery with deep learning.

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data.

Translatome analysis reveals microglia and astrocytes to be distinct regulators of inflammation in the hyperacute and acute phases after stroke.

Neuroinflammation is a hallmark of ischemic stroke, which is a leading cause of death and long-term disability. Understanding the exact cellular signaling pathways that initiate and propagate neuroinflammation after stroke will be critical for developing immunomodulatory stroke therapies. In particular, the precise mechanisms of inflammatory signaling in the clinically relevant hyperacute period, hours after stroke, have not been elucidated.

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE.

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.

Translatome analysis reveals microglia and astrocytes to be distinct regulators of inflammation in the hyperacute and acute phases after stroke.

Neuroinflammation is a hallmark of ischemic stroke, which is a leading cause of death and long-term disability. Understanding the exact cellular signaling pathways that initiate and propagate neuroinflammation after stroke will be critical for developing immunomodulatory stroke therapies. In particular, the precise mechanisms of inflammatory signaling in the clinically relevant hyperacute period, hours after stroke, have not been elucidated.

Reproduction, seasonal morphology, and juvenile growth in three Malagasy fruit bats.

The island nation of Madagascar is home to three endemic species of Old World fruit bat in the family Pteropodidae: , , and , all three of which are IUCN Red Listed under some category of threat. Delineation of seasonal limits in the reproductive calendar for threatened mammals can inform conservation efforts by clarifying parameters used in population viability models, as well as elucidate understanding of the mechanisms underpinning pathogen persistence in host populations. Here, we define the seasonal limits of a staggered annual birth pulse across the three species of endemic Madagascar fruit bat, known reservoirs for viruses of high zoonotic potential.

Single Cell Spatial Biology for Precision Cancer Medicine.

In cancer, complex ecosystems of interacting cell types play fundamental roles in tumor development, progression, and response to therapy. However, the cellular organization, community structure, and spatially defined microenvironments of human tumors remain poorly understood. With the emergence of new technologies for high-throughput spatial profiling of complex tissue specimens, it is now possible to identify clinically significant spatial features with high granularity.

Single-cell genomics in AML: extending the frontiers of AML research.

The era of genomic medicine has allowed acute myeloid leukemia (AML) researchers to improve disease characterization, optimize risk-stratification systems, and develop new treatments. Although there has been significant progress, AML remains a lethal cancer because of its remarkably complex and plastic cellular architecture. This degree of heterogeneity continues to pose a major challenge, because it limits the ability to identify and therefore eradicate the cells responsible for leukemogenesis and treatment failure.

Peripheral blood DNA methylation profiles predict future development of B-cell Non-Hodgkin Lymphoma.

Lack of accurate methods for early lymphoma detection limits the ability to cure patients. Since patients with Non-Hodgkin lymphomas (NHL) who present with advanced disease have worse outcomes, accurate and sensitive methods for early detection are needed to improve patient care. We developed a DNA methylation-based prediction tool for NHL, based on blood samples collected prospectively from 278 apparently healthy patients who were followed for up to 16 years to monitor for NHL development.

Community Level Youth-Led Interventions to Improve Maternal-Neonatal Outcomes in Low- and Middle-Income Countries: Protocol for a Systematic Review.

Introduction: On a global scale, women and childbearing people and neonates continue to die from preventable causes related to pregnancy or childbirth. Sustained and accelerated efforts are critical to improve maternal and neonatal health and well-being. Globally, youth are a growing population and have strength in their numbers.

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information.

Lymph node colonization induces tumor-immune tolerance to promote distant metastasis.

For many solid malignancies, lymph node (LN) involvement represents a harbinger of distant metastatic disease and, therefore, an important prognostic factor. Beyond its utility as a biomarker, whether and how LN metastasis plays an active role in shaping distant metastasis remains an open question. Here, we develop a syngeneic melanoma mouse model of LN metastasis to investigate how tumors spread to LNs and whether LN colonization influences metastasis to distant tissues.