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In recent years, improved sequencing technology and computational tools have made de novo genome assembly more accessible. Many approaches, however, generate either an unphased or only partially resolved representation of a diploid genome, in which polymorphisms are detected but not assigned to one or the other of the homologous chromosomes. Yet chromosomal phase information is invaluable for the understanding of phenotypic trait inheritance in the cases of compound heterozygosity, allele-specific expression or cis-acting variants. Here we use a combination of tools and sequencing technologies to generate a de novo diploid assembly of the human primary cell line WI-38. First, data from PacBio single molecule sequencing and Bionano Genomics optical mapping were combined to generate an unphased assembly. Next, 10x Genomics linked reads were combined with the hybrid assembly to generate a partially phased assembly. Lastly, we developed and optimized methods to use short-read (Illumina) sequencing of flow cytometry-sorted metaphase chromosomes to provide phase information. The final genome assembly was almost fully (94%) phased with the addition of approximately 2.5-fold coverage of Illumina data from the sequenced metaphase chromosomes. The diploid nature of the final de novo genome assembly improved the resolution of structural variants between the WI-38 genome and the human reference genome. The phased WI-38 sequence data are available for browsing and download at wi38.research.calicolabs.com. Our work shows that assembling a completely phased diploid genome de novo from the DNA of a single individual is now readily achievable.

Recent in situ multiplexed profiling techniques provide insight into microenvironment formation, maintenance, and transformation through a lens of localized cellular phenotype distribution. In this article, we introduce a method for recovering signatures of microenvironments from such data. We use topic models to identify characteristic cell types overrepresented in neighborhoods that serve as proxies for microenvironment. Furthermore, by assuming spatial coherence among neighboring microenvironments our model limits the number of parameters that need to be learned and permits data-driven decisions about the size of cellular neighborhoods. We apply this method to uncover anatomically known structures in mouse spleen-identifying distinct population of spleen B cells that are defined by their characteristic neighborhoods. Next, we apply the method to a dataset of triple-negative breast cancer tumors from 41 patients to study the structure of tumor-immune boundary. We uncover previously reported tumor-immune microenvironment near the tumor-immune boundary enriched for immune cells with high Indoleamine-pyrrole 2,3-dioxygenase (IDO) and Programmed death-ligand 1 (PD-L1) and a novel, immunosuppressed, microenvironment-enriched for cells expressing CD45 and FoxP3.

Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-β, regulatory T cells and IDO1+ PD-L1+ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.

Behavior and physiology are essential readouts in many studies but have not benefited from the high-dimensional data revolution that has transformed molecular and cellular phenotyping. To address this, we developed an approach that combines commercially available automated phenotyping hardware with a systems biology analysis pipeline to generate a high-dimensional readout of mouse behavior/physiology, as well as intuitive and health-relevant summary statistics (resilience and biological age). We used this platform to longitudinally evaluate aging in hundreds of outbred mice across an age range from 6 months to 3.4 years. In contrast to the assumption that aging can only be measured at the limits of animal ability via challenge-based tasks, we observed widespread physiological and behavioral aging starting in early life. Using network connectivity analysis, we found that organism-level resilience exhibited an accelerating decline with age that was distinct from the trajectory of individual phenotypes. We developed a method, Combined Aging and Survival Prediction of Aging Rate (CASPAR), for jointly predicting chronological age and survival time and showed that the resulting model is able to predict both variables simultaneously, a behavior that is not captured by separate age and mortality prediction models. This study provides a uniquely high-resolution view of physiological aging in mice and demonstrates that systems-level analysis of physiology provides insights not captured by individual phenotypes. The approach described here allows aging, and other processes that affect behavior and physiology, to be studied with sophistication and rigor.

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes. From the blood immunome of 1,001 individuals aged 8–96 years, the authors used deep learning to develop an inflammatory clock of aging (iAge) that tracks with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The main contributor to iAge is the chemokine CXCL9, which is shown to control endothelial cell senescence and function.

Purpose: In this paper, we describe a method for recovering the tissue properties directly from medical images and study the correlation of tissue (i.e., prostate) elasticity with the aggressiveness of prostate cancer using medical image analysis. Methods: We present a novel method that uses geometric and physical constraints to deduce the relative tissue elasticity parameters. Although elasticity reconstruction, or elastograph, can be used to estimate tissue elasticity, it is less suited for in-vivo measurements or deeply seated organs like prostate. We develop a method to estimate tissue elasticity values based on pairs of images, using a finite-element-based biomechanical model derived from an initial set of images, local displacements, and an optimization-based framework. Results: We demonstrate the feasibility of a statistically based classifier that automatically provides a clinical T-stage and Gleason score based on the elasticity values reconstructed from computed tomography images. Conclusion: We study the relative elasticity parameters by performing cancer grading/staging prediction and achieve up to 85% accuracy for cancer staging prediction and up to 77% accuracy for cancer grading prediction using a feature set, which includes recovered relative elasticity parameters and patient age information.

Isotopic labeling with deuterium oxide (D2O) is a common technique for estimatingin vivoprotein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity.

Tuberculosis (TB) is an infectious disease caused byMycobacterium tuberculosisthat is distinctly characterized by granuloma formation within infected tissues. Granulomas are dynamic and organized immune cell aggregates that limit dissemination, but can also hinder bacterial clearance. Consequently, outcome in TB is influenced by how granuloma structure and composition shift the balance between these two functions. To date, our understanding of what factors drive granuloma function in humans is limited. With this in mind, we used Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF) to profile 37 proteins in tissues from thirteen patients with active TB disease from the U.S. and South Africa. With this dataset, we constructed a comprehensive tissue atlas where the lineage, functional state, and spatial distribution of 19 unique cell subsets were mapped onto eight phenotypically-distinct granuloma microenvironments. This work revealed an immunosuppressed microenvironment specific to TB granulomas with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells and proliferating regulatory T cells. Interestingly, this microenvironment lacked markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of immunoregulatory proteins in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with latent or active TB infection and healthy controls across 29 cohorts spanning 14 countries. Notably, PD-L1 gene expression was found to correlate with TB progression and treatment response, supporting its potential use as a blood-based biomarker. Taken together, this study serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease.

The immune system comprises a complex network of specialized cells that protects against infection, eliminates cancerous cells, and regulates tissue repair, thus serving a critical role in homeostasis, health span, and life span. The subterranean-dwelling naked mole-rat (NM-R; Heterocephalus glaber) exhibits prolonged life span relative to its body size, is unusually cancer resistant, and manifests few physiological or molecular changes with advancing age. We therefore hypothesized that the immune system of NM-Rs evolved unique features that confer enhanced cancer immunosurveillance and prevent the age-associated decline in homeostasis. Using single-cell RNA-sequencing (scRNA-seq) we mapped the immune system of the NM-R and compared it to that of the short-lived, cancer-prone mouse. In contrast to the mouse, we find that the NM-R immune system is characterized by a high myeloid-to-lymphoid cell ratio that includes a novel, lipopolysaccharide (LPS)-responsive, granulocyte cell subset. Surprisingly, we also find that NM-Rs lack canonical natural killer (NK) cells. Our comparative genomics analyses support this finding, showing that the NM-R genome lacks an expanded gene family that controls NK cell function in several other species. Furthermore, we reconstructed the evolutionary history that likely led to this genomic state. The NM-R thus challenges our current understanding of mammalian immunity, favoring an atypical, myeloid-biased mode of innate immunosurveillance, which may contribute to its remarkable health span., A single-cell transcriptomic study of the immune system of the cancer-resistant naked mole-rat reveals that this animal lacks natural killer (NK) cells, thought to be crucial for cancer resistance. In contrast to dramatically expanded NK cell receptor and MHC-I gene families in human and mouse genomes, the naked mole-rat genome lacks the expansion of NK cell receptor genes and only has two MHC-I genes.