Modeling primitive and definitive erythropoiesis with induced pluripotent stem cells.

ABSTRACT: During development, erythroid cells are produced through at least 2 distinct hematopoietic waves (primitive and definitive), generating erythroblasts with different functional characteristics. Human induced pluripotent stem cells (iPSCs) can be used as a model platform to study the development of red blood cells (RBCs) with many of the differentiation protocols after the primitive wave of hematopoiesis. Recent advances have established that definitive hematopoietic progenitors can be generated from iPSCs, creating a unique situation for comparing primitive and definitive erythrocytes derived from cell sources of identical genetic background. We generated iPSCs from healthy fetal liver (FL) cells and produced isogenic primitive or definitive RBCs which were compared directly to the FL-derived RBCs. Functional assays confirmed differences between the 2 programs, with primitive RBCs showing a reduced proliferation potential, larger cell size, lack of Duffy RBC antigen expression, and higher expression of embryonic globins. Transcriptome profiling by scRNA-seq demonstrated high similarity between FL- and iPSC-derived definitive RBCs along with very different gene expression and regulatory network patterns for primitive RBCs. In addition, iPSC lines harboring a known pathogenic mutation in the erythroid master regulator KLF1 demonstrated phenotypic changes specific to definitive RBCs. Our studies provide new insights into differences between primitive and definitive erythropoiesis and highlight the importance of ontology when using iPSCs to model genetic hematologic diseases. Beyond disease modeling, the similarity between FL- and iPSC-derived definitive RBCs expands potential applications of definitive RBCs for diagnostic and transfusion products.

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis.

BACKGROUND: Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT. OBJECTIVES: To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status. MATERIALS AND METHODS: This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status. RESULTS: This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia. CONCLUSION: Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD. CLINICAL RELEVANCE STATEMENT: Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients. KEY POINTS: •Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.

Predictive Value of Deep Learning-derived CT Pectoralis Muscle and Adipose Measurements for Incident Heart Failure: Multi-Ethnic Study of Atherosclerosis.

Purpose: To develop a deep learning algorithm capable of extracting pectoralis muscle and adipose measurements and to longitudinally investigate associations between these measurements and incident heart failure (HF) in participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Materials and Methods: MESA is a prospective study of subclinical cardiovascular disease characteristics and risk factors for progression to clinically overt disease approved by institutional review boards of six participating centers (ClinicalTrials.gov identifier: NCT00005487). All participants with adequate imaging and clinical data from the fifth examination of MESA were included in this study. Hence, in this secondary analysis, manual segmentations of 600 chest CT examinations (between the years 2010 and 2012) were used to train and validate a convolutional neural network, which subsequently extracted pectoralis muscle and adipose (intermuscular adipose tissue (IMAT), perimuscular adipose tissue (PAT), extramyocellular lipids and subcutaneous adipose tissue) area measurements from 3031 CT examinations using individualized thresholds for adipose segmentation. Next, 1781 participants without baseline HF were longitudinally investigated for associations between baseline pectoralis muscle and adipose measurements and incident HF using crude and adjusted Cox proportional hazards models. The full models were adjusted for variables in categories of demographic (age, race, sex, income), clinical/laboratory (including physical activity, BMI, and smoking), CT (coronary artery calcium score), and cardiac MRI (left ventricular ejection fraction and mass (% of predicted)) data. Results: In 1781 participants (median age, 68 (IQR,61, 75) years; 907 [51%] females), 41 incident HF events occurred over a median 6.5-year follow-up. IMAT predicted incident HF in unadjusted (hazard ratio [HR]:1.14; 95% CI: 1.03-1.26) and fully adjusted (HR:1.16, 95% CI: 1.03-1.31) models. PAT also predicted incident HF in crude (HR:1.19; 95% CI: 1.06-1.35) and fully adjusted (HR:1.25; 95% CI: 1.07-1.46) models. Conclusion: The study demonstrates that fast and reliable deep learning-derived pectoralis muscle and adipose measurements are obtainable from conventional chest CT, which may be predictive of incident HF.©RSNA, 2023.

Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions.

CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capture the lower extremities in weight-bearing mode; and dual-energy CT, which operates at two different x-ray potentials to improve the contrast resolution to facilitate the assessment of tissue material compositions such as tophaceous gout deposits and bone marrow edema. Most recently, photon-counting CT (PCCT) has been introduced. PCCT is a technique that uses photon-counting detectors to produce an image with higher spatial and contrast resolution than conventional multidetector CT systems. In addition, postprocessing techniques such as three-dimensional printing and cinematic rendering have used CT data to improve the generation of both physical and digital anatomic models. Last, advancements in the application of artificial intelligence to CT imaging have enabled the automatic evaluation of musculoskeletal pathologies. In this review, the authors discuss the current state of the above CT technologies, their respective advantages and disadvantages, and their projected future directions for various musculoskeletal applications.

ActinoBase: tools and protocols for researchers working on Streptomyces and other filamentous actinobacteria.

Actinobacteria is an ancient phylum of Gram-positive bacteria with a characteristic high GC content to their DNA. The ActinoBase Wiki is focused on the filamentous actinobacteria, such as Streptomyces species, and the techniques and growth conditions used to study them. These organisms are studied because of their complex developmental life cycles and diverse specialised metabolism which produces many of the antibiotics currently used in the clinic. ActinoBase is a community effort that provides valuable and freely accessible resources, including protocols and practical information about filamentous actinobacteria. It is aimed at enabling knowledge exchange between members of the international research community working with these fascinating bacteria. ActinoBase is an anchor platform that underpins worldwide efforts to understand the ecology, biology and metabolic potential of these organisms. There are two key differences that set ActinoBase apart from other Wiki-based platforms: [1] ActinoBase is specifically aimed at researchers working on filamentous actinobacteria and is tailored to help users overcome challenges working with these bacteria and [2] it provides a freely accessible resource with global networking opportunities for researchers with a broad range of experience in this field.

Genome Engineering Human ESCs or iPSCs with Cytosine and Adenine Base Editors.

The ability to engineer specific mutations in human embryonic stem cells (ECSs) or induced pluripotent stem cells (iPSCs) is extremely important in the modeling of human diseases and the study of biological processes. While CRISPR/Cas9 can robustly generate gene knockouts (KOs) and gene loci modifications in coding sequences of iPSCs, it remains difficult to produce monoallelic mutations or modify specific nucleotides in noncoding sequences due to technical constraints.Here, we describe how to leverage cytosine (BE4max) and adenine (ABEmax) base editors to introduce precise mutations in iPSCs without inducing DNA double-stranded breaks. This chapter illustrates how to design and clone gRNAs, evaluate editing efficiency, and detect genomic edits at specific sites in iPSCs through the utilization of base editing technology.

Study of inherited thrombocytopenia resulting from mutations in ETV6 or RUNX1 using a human pluripotent stem cell model.

Inherited thrombocytopenia results in low platelet counts and increased bleeding. Subsets of these patients have monoallelic germline mutations in ETV6 or RUNX1 and a heightened risk of developing hematologic malignancies. Utilizing CRISPR-Cas9, we compared the in vitro phenotype of hematopoietic progenitor cells and megakaryocytes derived from induced pluripotent stem cell (iPSC) lines harboring mutations in either ETV6 or RUNX1. Both mutant lines display phenotypes consistent with a platelet-bleeding disorder. Surprisingly, these cellular phenotypes were largely distinct. The ETV6-mutant iPSCs yield more hematopoietic progenitor cells and megakaryocytes, but the megakaryocytes are immature and less responsive to agonist stimulation. On the contrary, RUNX1-mutant iPSCs yield fewer hematopoietic progenitor cells and megakaryocytes, but the megakaryocytes are more responsive to agonist stimulation. However, both mutant iPSC lines display defects in proplatelet formation. Our work highlights that, while patients harboring germline ETV6 or RUNX1 mutations have similar clinical phenotypes, the molecular mechanisms may be distinct.

Widening the bottleneck: Heterologous expression, purification, and characterization of the Ktedonobacter racemifer minimal type II polyketide synthase in Escherichia coli.

Enzyme assemblies such as type II polyketide synthases (PKSs) produce a wide array of bioactive secondary metabolites. While the molecules produced by type II PKSs have found remarkable clinical success, the biosynthetic prowess of these enzymes has been stymied by 1) the inability to reconstitute the bioactivity of the minimal PKS enzymes in vitro and 2) limited exploration of type II PKSs from diverse phyla. To begin filling this unmet need, we expressed, purified, and characterized the ketosynthase chain length factor (KS-CLF) and acyl carrier protein (ACP) from Ktedonobacter racemifer (Kr). Using E. coli as a heterologous host, we obtained soluble proteins in titers signifying improvements over previous KS-CLF heterologous expression efforts. Characterization of these enzymes reveals that KrACP has self-malonylating activity. Sedimentation velocity analytical ultracentrifugation (SV-AUC) analysis of holo-KrACP and KrKS-CLF indicates that these enzymes do not interact in vitro, suggesting that the acylated state of these proteins might play an important role in facilitating biosynthetically relevant interactions. These results lay important groundwork for optimizing the interaction between KrKS-CLF and KrACP and exploring the biosynthetic potential of other non-actinomycete type II PKSs.