bluuebunny/crossref_metadata_2025_split huggingface dataset:
AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity
Authors: Venkat Aarthi , Youlten Scott E. , Juan Beatriz P. San , Purcell Carley , Amodio Matthew , Burkhardt Daniel B. , Benz Andrew , Holst Jeff , McCool Cerys , Mollbrink Annelie , Lundeberg Joakim , van Dijk David , Goldstein Leonard D. , Kummerfeld Sarah , Krishnaswamy Smita , Chaffer Christine L. 
Published: May 2024

SummaryIdentifying functionally important cell states and structure within a heterogeneous tumor remains a significant biological and computational challenge. Moreover, current clustering or trajectory-based computational models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. To address this, we present Archetypal Analysis network (AAnet), a neural network that learns key archetypal cell states within a phenotypic continuum of cell states in single-cell data. Applied to single-cell RNA sequencing data from pre-clinical models and a cohort of 34 clinical breast cancers, AAnet identifies archetypes that resolve distinct biological cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Notably, archetypes identified in primary tumors are recapitulated in matched liver, lung and lymph node metastases, demonstrating that a significant component of intratumoral heterogeneity is driven by cell intrinsic properties. Using spatial transcriptomics as orthogonal validation, AAnet-derived archetypes show discrete spatial organization within tumors, supporting their distinct archetypal biology. We further reveal that ligand:receptor cross-talk between cancer and adjacent stromal cells contributes to intra-archetypal biological mimicry. Finally, we use AAnet archetype identifiers to validate GLUT3 as a critical mediator of a hypoxic cell archetype harboring a cancer stem cell population, which we validate in human triple-negative breast cancer specimens. AAnet is a powerful tool to reveal functional cell states within complex samples from multimodal single-cell data.
Opto-chemogenetic inhibition of L-type CaV1 channels in neurons through a membrane-assisted molecular linkage
Authors: Geng Jinli , Yang Yaxiong , Li Boying , Yu Zhen , Qiu Shuang , Zhang Wen , Gao Shixin , Liu Nan , Liu Yi , Wang Bo , Fan Yubo , Xing Chengfen , Liu Xiaodong 
Published: May 2024

SummaryAcute, specific, and robust inhibition of L-type Ca2+(CaV1) channels has been sought after for both research and therapeutic applications. Compared to other available CaV1 antagonists, genetically-encoded modulators, such as CMI (C-terminus mediated inhibition) peptides encoded by CaV1 DCT (distal C-terminus), hold great potentials due to its affirmative mechanisms of action on both gating and signaling. Here, we find that membrane-anchoring with a Ras tag could essentially help form a type of intramolecular-equivalent linkage, by which the tag anchored-peptide appears to dimerize with another protein or peptide on the membrane, supported by the evidence from patch-clamp electrophysiology and FRET imaging. We then design and implement the constitutive and inducible CMI modules, with appropriate dynamic ranges targeting the short and long variants of CaV1.3, both naturally occurring in neurons. Upon optical (infrared-responsive nanoparticles) and/or chemical (rapamycin) induction of FRB/FKBP binding, DCT peptides with no CMI in the cytosol acutely translocate onto the membrane via FRB-Ras, where the physical linkage requirement could be fulfilled. The peptides robustly produce acute and potent inhibitions on both recombinant CaV1.3 channels and neuronal CaV1 activities, and thus the Ca2+influx-neuritogenesis coupling. Validated through opto-chemogenetic induction, this prototype demonstrates channel modulation via membrane-assisted molecular linkage, promising broad applicability to diverse membrane proteins.
Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data
Authors: Zhu Bokai , Gao Sheng , Chen Shuxiao , Yeung Jason , Bai Yunhao , Huang Amy Y. , Yeo Yao Yu , Liao Guanrui , Mao Shulin , Jiang Sizun , Rodig Scott J. , Shalek Alex K. , Nolan Garry P. , Jiang Sizun , Ma Zongming 
Published: May 2024

AbstractCell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of “atlas” training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place.In this work, we introduceCell Spatio- andNeighborhood-informedAnnotation andPatterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhancesde novodiscovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells’ molecular profiles with cellular neighborhood and tissue image information.
Circulating PD-1hiCXCR5- and CXCR5+ CD4 T cells are elevated in patients with newly diagnosed Giant Cell Arteritis and predict relapse
Authors: Monjo Irene , Nieto-Carvalhal Beatriz , Uyaguari Mariela , García-Carazo Sara , Balsa Alejandro , de Miguel Eugenio , Miranda-Carús María-Eugenia 
Published: May 2024

ABSTRACTBACKGROUNDGiant cell arteritis (GCA) is a large/medium-vessel granulomatous vasculitis, and the PD-1/PD-L1 coinhibitory pathway seems to be implicated in its pathogenesis. CD4 T cells expressing high PD-1 levels, CD4+CXCR5-PD-1hiperipheral helper (Tph) and CD4+CXCR5+PD-1hifollicular helper T cells (Tfh), are key mediators of autoimmunity. Their frequencies are elevated in the peripheral blood of subjects with several autoimmune conditions but have not been investigated in GCA. Our objective was to study the frequency of circulating Tph (cTph) and Tfh (cTfh) in patients with newly diagnosed GCA (nGCA).METHODSProspective, non-interventional study on consecutive patients referred to our ultrasound GCA fast-track clinic over a period of 24 months. Peripheral blood was drawn immediately upon initial diagnosis. For each patient, an age and gender-matched healthy control (HC) was included. PBMCs isolated by Ficoll-Hypaque were examined by cytometry. Patients were subsequently treated with standard therapy according to the updated 2018 EULAR recommendations.RESULTS65 nGCA patients were included. As compared with HC, nGCA patients presented at baseline with an increased frequency of cTph and cTfh cells. Among the 46 patients who could be followed up for 12 months, 19 experienced a relapse. The baseline frequency of cTph and cTfh cells had been significantly lower in patients who relapsed as compared with those who did not. A cTph cell frequency <0.56 predicted relapse with a sensitivity of 90% and specificity of 93%.CONCLUSIONnGCA patients demonstrate increased baseline cTph and cTfh cell frequencies. Lower baseline proportions of cTph and cTfh cells associate with relapse.
Development of a humanized anti-FABP4 monoclonal antibody for treatment of breast cancer
Authors: Hao Jiaqing , Jin Rong , Yi Yanmei , Jiang Xingshan , Yu Jianyu , Xu Zhen , Schnicker Nicholas J. , Chimenti Michael S. , Sugg Sonia L. , Li Bing 
Published: May 2024

AbstractBackgroundBreast cancer, lung cancer, and colorectal cancer are the primary contributors to newly diagnosed cases among women, with breast cancer representing the second highest proportion of the total. The treatment protocols vary depends on different stages of breast cancer, and numerous clinical trials are ongoing based on the data derived from laboratory. Our studies demonstrate that circulating adipose fatty acid binding protein (A-FABP, or FABP4) links obesity-induced dysregulated lipid metabolism and breast cancer risk, thus offering a new target for breast cancer treatment.MethodsWe immunized FABP4 knockout mice with recombinant human FABP4 and screened hybridoma clones with specific binding to FABP4. The potential effects of antibodies on breast cancer cellsin vitrowere evaluated using migration, invasion, and limit dilution assays. Tumor progressionin vivowas evaluated in various types of tumorigenesis models including C57BL/6 mice, Balb/c mice, and SCID mice. The phenotype and function of immune cells in tumor microenvironment were characterized with multi-color flow cytometry. Tumor stemness was detected by ALDH assays. To characterize antigen-antibody binding capacity, we determined the dissociation constant of S-V9 against FABP4 via surface plasmon resonance. Further analyses in tumor tissue were performed using 10X Genomics Visium spatial single cell technology.ResultsHerein, we report the generation of humanized monoclonal antibodies blocking FABP4 activity for breast cancer treatment in mouse models. One clone, named 12G2, which significantly reduced circulating levels of FABP4 and inhibited mammary tumor growth, was selected for further characterization. After confirming the therapeutic efficacy of the chimeric 12G2 monoclonal antibody consisting of mouse variable regions and human IgG1 constant regions, 16 humanized 12G2 monoclonal antibody variants were generated by grafting its complementary determining regions to selected human germline sequences. Humanized V9 monoclonal antibody showed consistent results in inhibiting mammary tumor growth and metastasis by affecting tumor cell mitochondrial metabolism.ConclusionsOur current evidence suggest that targeting FABP4 with humanized monoclonal antibodies represents a novel strategy for the treatment of breast cancer and possibly other obesity- associated diseases.
Estradiol induces global changes in miRNA expression in endometrial cancer cells and upregulates oncogenic miR-182
Authors: Klicka Klaudia , Grzywa Tomasz M. , Wejman Jarosław , Ostrowska Joanna , Włodarski Paweł K. 
Published: May 2024

ABSTRACTEndometrial cancer (EC) occurs mainly in perimenopausal age. Risk factors are associated with unopposed estrogen stimulation of the endometrium. There are two main types of EC, EC1 and EC2. The pathogenesis of EC1 is estrogen-dependent. MiRNAs are small RNAs that regulate gene expression posttranscriptionally. They are induced by estrogens in different hormone-dependent neoplasias including breast cancer. However, little is known regarding their role in EC. Thus, this study aims to assess the role of the miRNA-estrogen axis in endometrial cancer cells. Estrogen-dependent endometrial cancer cell line Ishikawa was used in the study. Cells were incubated with estradiol, followed by RNA isolation. We used the microarray method to identify estradiol-induced miRNAs in EC cells. Then, we analyzed tissues derived from 45 patients (18 EC1, 12 EC2, and 15 healthy endometrium (HE)) that were cut using the Laser Capture Microdissection method. The expression of selected miRNAs and their targets was assessed using the RT-qPCR method. Ishikawa cells were transfected with miRNA mimic, miRNA inhibitor (anti-miRNA), and their scrambled controls. We identified 66 estrogen-upregulated miRNAs in endometrial cancer cells. Out of them, miR-182 was upregulated in EC1 compared to HE. We found that miR-182 is an oncomiR in EC since its increased expression promoted the proliferation of EC cells and decreased expression of miR-182 was associated with the inhibition of cancer cell proliferation. Moreover, miR-182 inhibition upregulated SMAD4 expression. Our study allowed us to better understand the role of estrogen in the pathogenesis of EC.
The 40 Hz flickering light restores synaptic plasticity and mitochondrial phenotype in experimental model of Alzheimer’s disease
Authors: Barzegar behrooz Amir , Aghanoori Mohamad-Reza , Khodagholi Fariba , Nazari Maryam , Latifi hamid , vosoghian Fatemeh , Anjomani Mojdeh , Lotfi Jaber , Ahmadiani Abolhassan , Eliassi Afsaneh , Nabavizadeh Fatemeh , Ghavami Saeid , soleimani Elham , Fahanik-babaei Javad 
Published: May 2024

AbstractAlzheimer’s disease (AD) is the most prevalent form of dementia and a public health priority. The causes of AD are not completely understood. Pathogenetic factors including mitochondrial dysfunction, oxidative stress, reduced energy status, and compromised ion channels contribute to the onset and progression of the disease. Flickering light therapy in experimental and clinical AD has shown promising outcomes. However, the mechanisms behind the effect of flickering light at the molecular and cellular level has not yet been fully investigated. In this study, we established streptozotocin (STZ)-induced AD models by intracerebroventricular (ICV) injection of STZ in Wistar rats and monitored their memory decline. Sham and AD rats were either exposed or not exposed to 40 Hz flickering light for 7 consecutive days after 7 days of STZ injection. Memory and cognition-related behavioral analysis, pathological, electrophysiological, and biochemical assessment of the brain tissue, and mitochondrial function assays were conducted after the treatment. Cognitive and memory impairment, examined by Morris water maze (MWM), novel object recognition (NOR), and passive avoidance (PA) test, was observed in the STZ-induced AD rats and light treatment improved these behaviors. STZ injection led to significant accumulation of reactive oxygen species (ROS) and amyloid beta (Aβ), decreased serotonin and dopamine levels, and mitochondrial respiration. The 40 Hz flickering light reversed all these parameters in the light treatment group. The synaptic plasticity of STZ-induced AD rats was severely affected, but flickering light prevented the loss of synaptic plasticity and activity in the light-treated AD rats. Additionally, flickering 40 Hz white light elevated the levels of mitochondrial metabolites and the current and possible opening of the mitochondrial calcium-sensitive potassium (mitoBKCa) channel which were significantly downregulated in AD rat neurons. The 40 Hz flickering light restored mitochondrial function and synaptic plasticity of neurons in AD rats and improved the cognition of animals; therefore, it can be a promising strategy to reduce AD progression.
Automated and closed clinical-grade manufacturing protocol produces potent NK cells against neuroblastoma cells and AML blasts
Authors: Jahan Farhana , Penna Leena , Luostarinen Annu , Veltman Laurens , Hongisto Heidi , Lähteenmäki Kaarina , Müller Sabine , Ylä-Herttuala Seppo , Korhonen Matti , Vettenranta Kim , Laitinen Anita , Salmenniemi Urpu , Kerkelä Erja 
Published: May 2024

AbstractNatural killer (NK) cells have great potential as allogeneic immune cell therapy due to their natural ability to recognize and kill tumor cells, and due to their apparent safety. This study describes the development of an immunotherapy option tailored for high-risk acute myeloid leukemia (AML) in adults and neuroblastoma in children. A GMP-compliant manufacturing protocol for the local production of functionally potent NK cells is detailed in the study, including a comprehensive description of the quality control strategy and considerations for product batch specifications in early clinical development. The protocol is based on the closed, automated CliniMACS Prodigy® platform (Miltenyi Biotec) and a modified Natural Killer Cell Transduction (NKCT) process without transduction and expansion. NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with cytokines (500 IU/ml IL-2, 140 IU/ml IL-15).Three CliniMACS Prodigy® NKCT processes were executed, demonstrating the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product for allogeneic use. Phenotypic and functional characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, even following a brief 12-hour activation period, the NK cells exhibited increased killing efficacy against CD33+ AML blasts isolated from patients and against SH-SY5Y neuroblastoma (NBL) target cellsin vitro, suggesting a potential therapeutic benefit for AML and NBL patients.
Alterations in Causal Functional Brain Networks in Alzheimer’s Disease: A resting-state fMRI study
Authors: Biswas Rahul , Sripada SuryaNarayana 
Published: May 2024

AbstractBackgroundAlterations in functional connectivity (FC) of the brain is known to predate the onset of clinical symptoms of Alzheimer’s disease (AD) by several decades. Identifying the altered functional brain networks in AD can help in its prognosis and diagnosis.ObjectiveFC analysis is predominantly correlational. However, correlation does not necessarily imply causation. This study aims to infer causal functional connectivity (CFC) from functional magnetic resonance imaging (fMRI) data and obtain the sub-networks of CFC that are altered in AD compared to cognitively normal (CN) subjects.MethodsWe used the recently developed Time-aware PC algorithm to infer CFC between brain regions. The CFC outcome was compared with correlation-based functional connectivity obtained by sparse partial correlation. Then, Network-based Statistics (NBS) was used to obtain CFC sub-networks that altered in AD subjects compared to healthy controls while correcting for multiple comparisons at 5% level of significance.ResultsOur findings identified causal brain networks involving the inferior frontal gyrus, superior temporal gyrus (temporal pole), middle temporal gyrus (temporal pole), and different lobes of the cerebellum to be significantly reduced in strength in AD compared to CN group (p-value = 0.0299; NBS corrected). In the sample dataset that has been analysed, no brain networks were found to exhibit significant increase in strength in AD compared to CN group at 5% level of significance with NBS correction.ConclusionsOur findings provide insights into disruptions in causal brain networks in AD. The corresponding brain regions are in agreement with published medical literature on brain regions impacted by AD. Our work establishes a methodology for finding causal brain networks that are affected by AD using TPC algorithm to compute subject-specific CFC and then using NBS for finding CFC subnetworks that alter between AD and CN groups. Larger datasets are expected to identify further subnetworks affected by AD.
Development and external validation of an ultrasound image-based deep learning model to estimate gestational age in the second and third trimesters of pregnancy using data from Garbh-Ini cohort: a prospective cohort study in North Indian population
Authors: Mishra Divyanshu , Chandramohan Varun , Sharma Nikhil , Gosain Mudita , Wadhwa Nitya , Natchu Uma Chandra Mouli ,   GARBH-Ini study group, Khurana Ashok , Noble J. Alison , Thiruvengadam Ramachandran , Desiraju Bapu Koundinya , Bhatnagar Shinjini 
Published: May 2024

AbstractAccurate estimation of gestational age (GA) is essential to plan appropriate antenatal care. Current GA estimation models rely on fetal biometry measurements, which are susceptible to ethnic and pathological variations in fetal growth, especially in the second and third trimesters of pregnancy. In this study, we challenge the current paradigm of estimating GA using fetal biometry, by using ultrasound (US) images and deep learning models which can automatically learn image features associated with GA. We developed deep learning models for GA estimation using US images taken at 18-32 weeks of pregnancy from 2207 participants of Garbh-Ini - a hospital-based prospective cohort of pregnant women in North India. Further, we designed a novel conformal prediction (CP) algorithm to detect and reject images when there is a data distribution shift, preventing erroneous predictions. Our best model, GArbh-Ini Ultrasound image-based Gestational age Estimator (GAUGE), which was trained on US images of the fetal head (9647 images from 2207 participants), had a mean absolute error (MAE) of 2.8 days when evaluated on an internal test dataset (N = 204). GAUGE is 44% and 35% more accurate than the widely used Hadlock and INTERGROWTH-21st biometry-based GA models, respectively on the internal test dataset. For an external test dataset (N = 311), collected retrospectively from The Ultrasound Lab, New Delhi, the same model achieved a MAE of 5.9 days. In addition, we show that GAUGE relies on the finer details in the image instead of the fetal biometry and that this leads to a similar performance across small for gestational age (SGA) and appropriate for gestational age (AGA) groups. The ability of GAUGE to consider image features beyond derived biometry suggests that GAUGE offers a better choice for populations with a high prevalence of fetal growth restriction.
Dimension Reduction using Local Principal Components for Regression-based Multi-SNP Analysis in 1000 Genomes and the Canadian Longitudinal Study on Aging (CLSA)
Authors: Yavartanoo Fatemeh , Brossard Myriam , Bull Shelley B. , Paterson Andrew D. , Yoo Yun Joo 
Published: May 2024

AbstractFor genetic association analysis based on multiple SNP regression of genotypes obtained by dense DNA sequencing or array data imputation, multi-collinearity can be a severe issue causing failure to fit the regression model. In this study, we proposed a method of Dimension Reduction using Local Principal Components (DRLPC) which aims to resolve multi-collinearity by removing SNPs under the assumption that the remaining SNPs can capture the effect of a removed SNP due to high linear dependency. This approach to dimension reduction is expected to improve the power of regression-based statistical tests. We apply DRLPC to chromosome 22 SNPs of two data sets, the 1000 Genomes Project (phase 3) and Canadian Longitudinal Study on Aging (CLSA), and calculated Variance Inflation Factors (VIF) in various SNP-sets before and after implementing DRLPC as a metric of collinearity. Notably, DRLPC addresses multi-collinearity by excluding variables with a VIF exceeding a predetermined threshold (VIF=20), thereby improving applicability for subsequent regression analyses. The number of variables in a final set for regression analysis is reduced to around 20% on average for larger-sized genes, whereas for smaller ones, the proportion is around 48%; suggesting that DRLPC is more effective for larger genes. We also compare the power of several multi-SNP statistics constructed for gene-specific analysis to evaluate power gains achieved by DRLPC. In simulation studies based on 100 genes with ≤500 SNPs per gene, DRLPC effectively increased the power of the multiple regression Wald test from 60% to around 80%.