Increased iron uptake by splenic hematopoietic stem cells promotes TET2-dependent erythroid regeneration.

Hematopoietic stem cells (HSCs) are capable of regenerating the blood system, but the instructive cues that direct HSCs to regenerate particular lineages lost to the injury remain elusive. Here, we show that iron is increasingly taken up by HSCs during anemia and induces erythroid gene expression and regeneration in a Tet2-dependent manner. Lineage tracing of HSCs reveals that HSCs respond to hemolytic anemia by increasing erythroid output. The number of HSCs in the spleen, but not bone marrow, increases upon anemia and these HSCs exhibit enhanced proliferation, erythroid differentiation, iron uptake, and TET2 protein expression. Increased iron in HSCs promotes DNA demethylation and expression of erythroid genes. Suppressing iron uptake or TET2 expression impairs erythroid genes expression and erythroid differentiation of HSCs; iron supplementation, however, augments these processes. These results establish that the physiological level of iron taken up by HSCs has an instructive role in promoting erythroid-biased differentiation of HSCs.

ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data.

MOTIVATION: Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS: In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION: Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).

NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics (ST) can reveal the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify spatially localized processes underlying a tissue's function. Existing tools to detect spatially variable genes assume a constant noise variance across spatial locations. This assumption might miss important biological signals when the variance can change across locations. RESULTS: In this article, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST models gene expression as a function of spatial location and allows the noise to vary spatially. NoVaTeST then statistically compares this model to one with constant noise and detects genes showing significant spatial noise variation. We refer to these genes as "noisy genes." In tumor samples, the noisy genes detected by NoVaTeST are largely independent of the spatially variable genes detected by existing tools that assume constant noise, and provide important biological insights into tumor microenvironments. AVAILABILITY AND IMPLEMENTATION: An implementation of the NoVaTeST framework in Python along with instructions for running the pipeline is available at https://github.com/abidabrar-bracu/NoVaTeST.

Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions.

Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.