Individual Cells Research Articles

EPCO-11. BIASES IN HEALTHY BRAIN SINGLE-CELL DATASETS: FINDING THE BEST COMPARISON FOR GLIOBLASTOMA STUDIES

Abstract Tumor cells undergo metabolic alterations that support their proliferation and simultaneously modify immune populations, resulting in an immunosuppressive tumor microenvironment. This study aims to compare the metabolic expression of immune cells within the tumor microenvironment to those in a healthy environment. However, there is a lack of single-cell transcriptomic data for normal brain cells, necessitating an analysis of different methods to compare immune cells in tumors with those in a healthy brain. To address this, we explored cell-to-cell metabolic variability using public single-cell RNA-seq (scRNA-seq) data of glioblastoma (n=338564), and from immune cells in the brains of post-mortem individuals (n=14177), fetuses (n=40000), and epilepsy patients (n=466), as well as from immune blood cells (n=329762). We employed ssGSEA to generate activity scores for 70 key metabolic pathways from KEGG for each individual cell. Known marker genes were utilized to identify normal cell populations (macrophages, T cells, oligodendrocytes) in datasets lacking cell type annotations. Our results revealed that blood cells exhibited high metabolic activity in macrophages but showed moderate activity in other cell types. Immune blood cells also demonstrated high oxidative phosphorylation (OXPHOS) but low fatty acid activity. Fetal brain cells displayed a lack of heterogeneity and showed high fatty acid activity with low OXPHOS activity. Brain cells from post-mortem individuals displayed heterogeneity but exhibited low metabolic activity across nearly all cells and pathways. Additionally, the hierarchy of pathway activity differed from the other datasets. Epilepsy data revealed a metabolic activity pattern similar to tumor-infiltrating immune cells, with comparable activity hierarchy and heterogeneity, although the sample size was limited for detailed comparison. Understanding the differences between types of datasets is important to choose the most appropriate comparison, allowing us to observe the metabolic alterations in immune cells caused by glioblastoma and to develop better immunotherapy strategies.

ANGI-11. SEX DIFFERENCES IN SOX2 GLIOBLASTOMA EXPRESSION OUTSIDE MRI-DEFINED CONTRAST ENHANCEMENT AT AUTOPSY

Abstract INTRODUCTION Sex-determining region Y-box 2 (SOX2) has recently been used as a marker for pluripotency to identify cellular glioblastoma invasion pathologically. This can be used to identify areas of tumor invasion consisting of sparse individual cells, likely highlighting the furthest margin of tumor invasion. This study aligned large format SOX2 stained tissue samples to conventional MRI to determine demographic factors that impact the presence of SOX2 positive invasion outside the T1 contrast-enhanced tumor margin. METHODS The cohort for this study consisted of 36 IDH-wildtype glioblastoma patients (23 male, 13 female, mean age=61.9). Tissue samples from areas of suspected tumor invasion and normal tissue were collected at autopsy, stained for SOX2, digitized at 40X resolution, and automatically segmented for SOX2 positive staining cell percent (SOX2+%) at MR resolution using a color deconvolution algorithm. Contrast enhanced T1 scans from acquisitions close to death were annotated manually for tumor presence and aligned to the T2-weighted FLAIR image. Tissue data was then aligned to MR images using a custom in-house software that relies on manually defined control points to generate a nonlinear transform that warps tissue into MR space while correcting for tissue shrinkage. Mean SOX2+% was computed for each patient outside of contrast enhancement and compared across age and sex as demographic factors. RESULTS Males were found to have a higher SOX2+% than females outside contrast enhancement (t=2.09, p=0.045), while no association with age was observed (r=0.146, p=0.397). CONCLUSIONS Sex differences were observed for non-enhancing SOX2 positivity, which may relate to the more aggressive tumors identified in male patients in some studies. Further research into the mechanisms behind sex differences in tumor growth patterns and molecular characteristics impacting the infiltration of tumor beyond the MR defined region is needed.

Differential vulnerability among cell types in the neurovascular unit: Description and mechanisms.

Currently, successful preclinical cerebroprotective agents fail to translate effectively into clinical practice suggesting the need for a comprehensive evaluation of all aspects of brain function. Selective vulnerability refers to the specific regional response of the brain following global ischemia, with observed patterns of vulnerability attributed to the distribution of neuronal subtypes and the functions of respective brain regions. Conversely, the concept of differential vulnerability pertains to the cell-type-specific reactions to cerebral ischemia, dictated by the biological characteristics of individual cells. This review aims to explore these vulnerability hypotheses and elucidate potential underlying cellular mechanisms.

Single-cell sequencing reveals cellular heterogeneity of nucleus pulposus in intervertebral disc degeneration

The nucleus pulposus (NP) plays a vital role in intervertebral disc degeneration (IVDD). Previous studies have revealed cellular heterogeneity in NP tissue during IVDD progression. However, the cellular and molecular alterations of diverse cell clusters during IVDD remain to be fully elucidated. NP tissues were isolated from patients with different grades of IVDD undergoing discectomy, and then subjected to single-cell RNA sequencing (scRNA-seq). Cell subsets were identified based on unbiased clustering of gene expression profiles. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to determine the molecular features of diverse cell clusters. Monocle analysis was used to illustrate the differentiation trajectories of chondrocytes. Additionally, CellPhoneDB analysis revealed potential interactions between chondrocytes and other cells during IVDD. Based on the expression profiles of 47,610 individual cells, eight putative clusters including chondrocytes, endothelial cells, fibroblasts, macrophages, mural cells, osteoclasts, proliferating stromal cells and T cells were identified. The chondrocyte cluster was classified into three subsets, C1-C3, which were associated with stress-resistance, fibrosis and inflammatory responses, respectively. Pseudo-time trajectories suggested that chondrocytes gradually differentiated into fibroblasts during IVDD. Immune cells including cDC2s, macrophages and monocytes were identified. Further analysis showed that chondrocytes might communicate with immune cells via the MIF, TNFSF9, SPP1 and CCL4L2 signaling pathways. In addition, we found that invading endothelial cells might interact with chondrocytes through the COL4A1, CXCL12, VEGFA and SEMA3E signaling pathways. Our results reveal the cellular complexity and phenotypic characteristics of NP tissues at single-cell resolution, which will contribute to the in-depth investigation of preventative and regenerative strategies for IVDD.

Characteristics of stem sings of EpCAM-negative and EpCAM-positive tumor cells in primary tumor and 2D and 3D cultures in breast cancer

One of the predictors of adverse prognosis in breast cancer is the overexpression of the EpCAM protein. However, a signifcant part of tumor cells have low or no EpCAM expression. The molecular features and the ability to grow in 2D and 3D cultures, indirectly refecting metastatic potential of tumor cells without EpCAM expression, have not been sufficiently studied.The aim of the study was to compare phenotypic variants of stem cells and EMT depending on the EpCAM expression in the primary tumor of breast cancer and 2D and 3D cultures.Material and Methods. The study included 7 patients with invasive breast carcinoma of no special type. Luminal A subtype was found in 2/7 patients (29 %), and luminal B HER2-negative molecular subtype was found in 5/7 patients (71 %). The patients didn’t have neoadjuvant chemotherapy. Cells from the primary tumor were cultured in 2D and 3D. The primary tumor cells, 2D and 3D cultures were phenotyped using fow cytometry with antibodies targeting: CD45, EpCAM (CD326), CD44, CD24, N-cadherin (CD325), and CD133, CK7/8, EpCAM (CD326).Results. No significant differences were found in the frequency of detection and the number of cells exhibiting stem cell features among EpCAM- and EpCAM+ tumor cells, 2D and 3D cultures. All phenotypic variants of co-expression of stemness markers CD44, CD24, CD133, and ALDH were present both in the primary tumor and 2D/3D cultures. Stem cells in mammospheres had features of both epithelial and hybrid EMT phenotypes. In primary tumors and 2D/3D cultures, the smallest proportion consisted of stem cells with the CD44+CD24- phenotype and cells co-expressing CD44+CD24- with other stemness markers, while the largest proportion comprised stem cells with ALDH+ and ALDH+CD133+ phenotypes. Marked intra- and inter-tumor heterogeneity in the phenotypic composition of primary tumors, 2D, and 3D cultures was noted.Conclusion. The results indicate that EpCAM-negative and EpCAM-positive tumor cells of luminal molecular subtype of breast cancer are capable of exhibiting various phenotypic variants of stemness and EMT in the primary tumor and 2D and 3D cultures. In most cases, individual tumor cells show co-expression of several stemness markers. The phenotypic composition of primary tumors, 2D and 3D cultures is characterized by pronounced intra- and inter-tumor heterogeneity.

Reproductomics: Exploring the Applications and Advancements of Computational Tools

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

A High-Fidelity Computational Model for Predicting Blood Cell Trafficking and 3D Capillary Hemodynamics in Retinal Microvascular Networks.

To present a first principle-based, high-fidelity computational model for predicting full three-dimensional (3D) and time-resolved retinal microvascular hemodynamics taking into consideration the flow and deformation of individual blood cells. The computational model is a 3D fluid-structure interaction model based on combined finite volume/finite element/immersed-boundary methods. Three in silico microvascular networks are built from high-resolution in vivo motion contrast images of the superficial capillary plexus in the parafoveal region of the human retina. The maximum tissue area represented in the model is approximately 500 × 500 µm2, and vessel lumen diameters ranged from 5.5 to 25 µm covering capillaries, arterioles, and venules. Blood is modeled as a suspension of individual blood cells, namely, erythrocytes (RBC), leukocytes (WBC), and platelets in plasma. An accurate and detailed biophysical modeling of each blood cell and their flow-induced deformation is considered. A physiological, pulsatile boundary condition corresponding to an average cardiac cycle of 0.9 second is used. Detailed quantitative data and analysis of 3D retinal microvascular hemodynamics are presented, and their relationship to RBC flow dynamics is illustrated. Blood velocity is shown to have temporal oscillations superimposed on the background pulsatile variation, which arise because of the way RBCs partition at vascular junctions, causing repeated clogging and unclogging of vessels. Temporal variations in RBC velocity and hematocrit are anti-correlated in a given vessel, but their time-averaged distributions are positively correlated across the network. Whole blood velocity is 65% to 85% of RBC velocity, with the discrepancy related to the formation of an RBC-free region, adjacent to the vascular endothelium and typically 0.8 to 1.8 µm thick. The 3D velocity and RBC concentration profiles are shown to be oppositely skewed with respect to each other, because of the way that RBCs "hug" the apex of each bifurcation. RBC deformation is predicted to have biphasic behavior with respect to vessel diameter, with minimal cell length for vessels approximately 7 µm in diameter. The wall shear stress (WSS) exhibits a strongly 3D distribution with local regions of high value and gradient spanning a range of 10 to 80 dyn/cm2. WSS is highest where there is faster flow, greater curvature of the vessel wall, capillary bifurcations, and at locations of RBC crowding and associated thinning of the cell-free layer. This study highlights the usefulness of high-fidelity cell-resolved modeling to obtain accurate and detailed 3D, time-resolved retinal hemodynamic parameters that are not readily available through noninvasive imaging approaches. The results presented are expected to complement and enhance the interpretation of in vivo data, as well as open new avenues to study retinal hemodynamics in health and disease.

High level of aneuploidy and recurrent loss of chromosome 11 as relevant features of somatotroph pituitary tumors

BackgroundSomatotroph neuroendocrine pituitary tumors (sPitNET) are a subtype of pituitary tumors that commonly cause acromegaly. Our study aimed to determine the spectrum of DNA copy number abnormalities (CNAs) in sPitNETs and their relevance.MethodsA landscape of CNAs in sPitNETs was determined using combined whole-genome approaches involving low-pass whole genome sequencing and SNP microarrays. Fluorescent in situ hybridization (FISH) was used for microscopic validation of CNAs. The tumors were also subjected to transcriptome and DNA methylation analyses with RNAseq and microarrays, respectively.ResultsWe observed a wide spectrum of cytogenetic changes ranging from multiple deletions, recurrent chromosome 11 loss, stable genomes, to duplication of the majority of the chromosomes. The identified CNAs were confirmed with FISH. sPitNETs with multiple duplications were characterized by intratumoral heterogeneity in chromosome number variation in individual tumor cells, as determined with FISH. These tumors were separate CNA-related sPitNET subtype in clustering analyses with CNA signature specific for whole genome doubling-related etiology. This subtype encompassed GNAS-wild type, mostly densely granulated tumors with favorable expression level of known prognosis-related genes, notably enriched with POUF1/NR5A1-double positive PitNETs. Chromosomal deletions in sPitNETs are functionally relevant. They occurred in gene-dense DNA regions and were related to genes downregulation and increased DNA methylation in the CpG island and promoter regions in the affected regions. Recurrent loss of chromosome 11 was reflected by lowered MEN1 and AIP. No such unequivocal relevance was found for chromosomal gains. Comparisons of transcriptomes of selected most cytogenetically stable sPitNETs with tumors with recurrent loss of chromosome 11 showed upregulation of processes related to gene dosage compensation mechanism in tumors with deletion. Comparison of stable tumors with those with multiple duplications showed upregulation of processes related to mitotic spindle, DNA repair, and chromatin organization. Both comparisons showed upregulation of the processes related to immune infiltration in cytogenetically stable tumors and deconvolution of DNA methylation data indicated a higher content of specified immune cells and lower tumor purity in these tumors.ConclusionssPitNETs fall into three relevant cytogenetic groups: highly aneuploid tumors characterized by known prognostically favorable features and low aneuploidy tumors including specific subtype with chromosome 11 loss.

Transient proliferation by reversible YAP and mitogen-control of the cyclin D1/p27 ratio.

Hippo-YAP signaling orchestrates epithelial tissue repair and is therefore an attractive target in regenerative medicine. Yet it is unresolved how YAP integrates with mitogen signaling and contact inhibition to control the underlying transient proliferative response. Here we show that reduced contact inhibition, increased mitogen signaling, and YAP-TEAD activation converge on increasing the nuclear cyclin D1/p27 protein ratio during G1 phase, towards a threshold ratio that dictates whether individual cells enter or exit the cell cycle. YAP increases this ratio indirectly, in concert with mitogen signaling, by increasing EGFR and other receptors that signal primarily through ERK. After a delay, contact inhibition suppresses YAP activity which gradually downregulates mitogen signaling and the cyclin D1/p27 ratio. Increasing YAP activity by ablating the suppressor Merlin/NF2 reveals a balancing mechanism in which YAP suppression and contact inhibition of proliferation can be recovered but only at higher local cell density. Thus, critical for tissue repair, robust proliferation responses result from the YAP-induced and receptor-mediated prolonged increase in the cyclin D1/p27 ratio, which is only reversed by delayed suppression of receptor signaling after contact inhibition of YAP.

Photoclick and Release for Spatiotemporally Localized Theranostics of Single Cells via In Situ Generation of 1,3‐Diaryl‐1H‐benzo[f]indazole‐4,9‐dione

Bioorthogonal click‐release chemistry is a cutting‐edge tool for exploring and manipulating biomolecule functions in native biological systems. However, it is challenging to achieve the precise regulation or therapy of individual cells via click‐release strategies driven by proximity and thermodynamics. Herein, we propose a novel photoclick‐release approach based on a photo‐induced cycloaddition between 4,4'‐bis(N‐arylsydnone) or C‐bithienyl‐diarylsydnone and 2‐arylamino‐naphthoquinone via irradiation with 405 or 485 nm light. It constructs 1,3‐diaryl‐1H‐benzo[f]indazole‐4,9‐dione (BIZON) as a pharmacophore while releases an arylamine for fluorescence turn‐on probing. Both of the photoclick reagents were tailored by connecting to the triphenyl phosphonium delivery motif for enrichment in the mitochondria of live cells. This enables an intracellular photoclick and release under the control of 405 or 485 nm light. We then discovered that the in situ photo‐generated BIZON is capable of photosensitizing upon 485 or 520 nm light to produce singlet oxygen inside the mitochondria under aerobic conditions. Therefore, we realized wash‐free fluorescence tracking and subsequent anti‐cancer efficacy at single‐cell resolution by using global illumination, which provides a foundation for wavelength‐gated single‐cell theranostics.

The Effect of the Acid-Base Imbalance on the Shape and Structure of Red Blood Cells.

Red blood cells respond to fluctuations in blood plasma pH by changing the rate of biochemical and physical processes that affect the specific functions of individual cells. This study aimed to analyze the effect of pH changes on red blood cell morphology and structure. The findings revealed that an increase or decrease in pH above or below the physiological level of pH 7.4 results in the transformation of discocytes into echinocytes and causes significant alterations in the membrane, including its roughness, cytoskeleton structure, and the cell's elastic modulus. Furthermore, the study shown a strong connection between critical acidosis and alkalosis with increased intracellular reactive oxygen species production.

PhotoclickandReleaseforSpatiotemporallyLocalized TheranosticsofSingleCells viaIn Situ Generation of 1,3-Diaryl-1H-benzo[f]indazole-4,9-dione.

Bioorthogonal click-release chemistry is a cutting-edge tool for exploringand manipulating biomolecule functionsin native biological systems. However, it is challenging to achieve the precise regulation or therapy of individual cells via click-release strategies driven by proximity and thermodynamics. Herein, we propose a novel photoclick-release approach based on a photo-induced cycloaddition between 4,4'-bis(N-arylsydnone) orC-bithienyl-diarylsydnone and 2-arylamino-naphthoquinone via irradiation with405 or 485 nm light. Itconstructs 1,3-diaryl-1H-benzo[f]indazole-4,9-dione (BIZON) as a pharmacophore while releases an arylamine for fluorescence turn-on probing. Both of the photoclick reagents were tailored byconnecting to the triphenyl phosphoniumdeliverymotif for enrichment in the mitochondria of live cells.This enablesan intracellular photoclick and release under the control of 405 or 485 nm light. We then discovered that the in situ photo-generated BIZON is capable of photosensitizing upon 485 or 520 nm light to produce singlet oxygen inside the mitochondria under aerobic conditions. Therefore, werealized wash-free fluorescence tracking and subsequent anti-cancer efficacy at single-cell resolution by using global illumination, which provides a foundation for wavelength-gated single-cell theranostics.

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping

The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific analytical tools. Herein, we propose a SERS-based platform for the classification of acute lymphoblastic T-cell leukemia (T-ALL) and chronic myeloid leukemia (CML) through the combination of machine learning and microfluidic chips. The ordered arrays in microfluidic channels reshape the microscopic flow field and contacting interfaces, facilitating the uniform and efficient capture of tumor cells. To enable phenotypic analysis, spectrally orthogonal SERS aptamer nanoprobes were applied, providing composite spectral signatures of individual cells in accordance with surface protein expression. Further, machine learning algorithms were employed to analyze the SERS signatures automatically, resulting in an accuracy of 98.6 % for 73 clinical blood samples. The results demonstrate that this platform holds promising potential for clinical leukemia diagnosis and precision medicine.

Microwell-assembled aluminum substrates for enhanced single-cell analysis: A novel approach for cancer cell profiling by Raman spectroscopy

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR).We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform.Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine.

A machine-learning-based algorithm for bone marrow cell differential counting

BackgroundDifferential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed. ObjectiveThis study developed and validated an artificial intelligence (AI)-based algorithm for identifying and classifying nucleated cells in BM smears. MethodsIn the development phase, a mask region–based convolutional neural network (Mask R-CNN)-based AI model was trained to detect and classify individual BM cells. We used a large data set of expert-annotated images representing a variety of disease categories. The BM slides were stained with Liu’s stain or Wright–Giemsa stain. Consensus meetings were held to ensure experts from different institutes applied consistent criteria in classifying cells. Subsequently, the performance of the AI algorithm in identifying cell images and determining cell ratios was evaluated using a multinational clinical dataset. ResultsThe AI model was trained on 542 slides (85.1 % stained with Liu’s stain and 14.9 % with Wright–Giemsa stain) containing 597,222 annotated cells. It achieved an accuracy of 0.94 for the testing dataset containing 26,170 cells. The performance of the AI model was further validated using another multinational real-world dataset (data obtained from three centers in Taiwan and one in the United States) comprising 200,639 cells. The AI model achieved an accuracy of 0.881 in classifying individual cells and demonstrated high precision in classifying blasts (0.927), bands and polymorphonuclear neutrophils (0.955), plasma cells (0.930), and lymphocytes (0.789). When the differential counting percentage of each cell type was assessed, a strong correlation (ρ > 0.8) between the AI and manual methods was observed for most cell categories. ConclusionsIn this study, an AI algorithm was developed and clinically validated using large, multinational datasets. Our algorithm can locate and classify BM cells simultaneously and has potential clinical applicability for automating BM differential counting.

MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning

Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integratingthegene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methodsin cell classification. In this work, we propose MVCLST, a multi-view comparative learningmethod to analyze spatial transcriptomicsdata for accurate cell type classification. MVCLSTconstructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to clusterthe learned featuresfor cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomicsdata analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. Italso outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulbdata.

Bioprinting of Aptamer-Based Programmable Bioinks to Modulate Multiscale Microvascular Morphogenesis in 4D.

Dynamic growth factor presentation influences how individual endothelial cells assemble into complex vascular networks. Here, programmable bioinks are developed that facilitate dynamic vascular endothelial growth factor (VEGF) presentation to guide vascular morphogenesis within 3D-bioprinted constructs. Aptamer's high affinity is leveraged for rapid VEGF sequestration in spatially confined regions and utilized aptamer-complementary sequence (CS) hybridization to tune VEGF release kinetics temporally, days after bioprinting. It is shown that spatial resolution of programmable bioink, combined with CS-triggered VEGF release, significantly influences the alignment, organization, and morphogenesis of microvascular networks in bioprinted constructs. The presence of aptamer-tethered VEGF and the generation of instantaneous VEGF gradients upon CS-triggering restricted hierarchical network formation to the printed aptamer regions at all spatial resolutions. Network properties improved as the spatial resolution decreased, with low-resolution designs yielding the highest network properties. Specifically, CS-treated low-resolution designs exhibited significant vascular network remodeling, with an increase in vessel density(1.35-fold), branching density(1.54-fold), and average vessel length(2.19-fold) compared to non-treated samples. The results suggest that CS acts as an external trigger capable of inducing time-controlled changes in network organization and alignment on-demand within spatially localized regions of a bioprinted construct. It is envisioned that these programmable bioinks will open new opportunities for bioengineering functional, hierarchically self-organized vascular networks within engineered tissues.

Thermal uniformity analysis of a hybrid battery pack using integrated phase change material, metal foam, and counterflow minichannels

This study investigates the thermal performance and temperature uniformity of a hybrid battery thermal management system (BTMS) that integrates phase change material (PCM), metal foam, and minichannels. Computational fluid dynamics is used to model the PCM melting process and heat transfer between all components. The primary goal of the work is to investigate BTMS architectures which can enhance thermal uniformity and prevent critical temperature rise in a high-voltage battery pack under fast discharging and real-world driving cycle. Four BTMS designs are compared. The design that integrates PCM, metal foam, and counterflow minichannels is shown to have the best performance. At low pumping power (coolant Reynolds number Re = 10), this design reduces the peak battery temperature by 11.5 K compared to a design employing pure PCM only. This configuration also ensures a temperature difference of less than 5 K among individual battery cells, addressing thermal safety considerations and extending battery lifespan. Further analysis revealed that the inclusion of metal foam delays PCM melting, enhances both system and battery thermal uniformity, and offers a higher performance-to-weight ratio compared to designs without metal foam. Although wavy-shaped minichannels offer minimal temperature improvement (0.3 K) over straight minichannels, their higher cost and increased pumping power requirements do not justify their practicality. Under both fast discharging and real driving conditions, the first design with pure PCM provides uniform heat distribution within batteries but fails to maintain the maximum battery temperature within the optimal range. Overall, this study highlights the effectiveness of the proposed hybrid BTMS design in providing uniform temperature distribution and maintaining the maximum battery temperature within the optimal range under harsh environmental conditions, fast discharging, and the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

Differential gliding motility responses of Chryseobacterium sp. strain PMSZPI isolated from uranium ore deposit on hard and soft substrates

The Bacteroidota bacterium, Chryseobacterium sp. strain PMSZPI isolated from sub-surface soil of uranium ore deposit was shown to move on solid surfaces via gliding motility resulting in the formation of thin spreading colonies. In this study, we attempted to understand the influence of the surfaces, soft or hard/rigid, on the motility behaviour of PMSZPI cells. The computational tool T9GPred in combination with LC-MS/MS analysis established the presence of orthologs of vital gliding motility proteins in PMSZPI. We analyzed the single cell or population motility phenotypes of PMSZPI under spreading and non-spreading conditions. A low percentage of agar or soft agar (0.35%) with low nutrient levels induced more active gliding motility in individual cells leading to increased colony spreading. Microscopic analyses indicated the self-assembly of the gliding cells into irregular edged or spherical microcolonies based on the agar concentration. Cells moved at a speed of 0.2 to 0.6 µm s-1 on low-percentage gliding permissive agar (0.35%) surface in contrast to significant inhibition of motility on rigid or hard agar (1.5%) surface. RNA sequencing and real-time quantitative PCR (qPCR) analysis revealed increased expression of gliding motility genes under low agar conditions consistent with increased spreading behaviour. These findings provide the first glimpse into the gliding motility behaviour of a Bacteroidota bacterium from metal enriched environment that apparently could have implications on bacterial adaptation to changing surface environments.

The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.

The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.