Transcriptomic signatures of oxytosis/ferroptosis are enriched in Alzheimer’s disease by Antonio Currais, Kayla Sanchez, David Soriano-Castell, Nawab John Dar, K. Garrett Evensen, Salvador Soriano, Pamela Maher

“…It is shown that the different signatures of oxytosis/ferroptosis are enriched to varying extents in the brains of AD mice and human AD patients. …”
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Comparison of spatial transcriptomics technologies using tumor cryosections by Anne Rademacher, Alik Huseynov, Michele Bortolomeazzi, Sina Jasmin Wille, Sabrina Schumacher, Pooja Sant, Denise Keitel, Konstantin Okonechnikov, David R. Ghasemi, Kristian W. Pajtler, Jan-Philipp Mallm, Karsten Rippe

“…Results Our analysis reveals that automated imaging-based spatial transcriptomics methods are well-suited to delineate the intricate MBEN microanatomy and capture cell-type-specific transcriptome profiles. …”
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Massively parallel sequencing and analysis of the Necator americanus transcriptome. by Cinzia Cantacessi, Makedonka Mitreva, Aaron R Jex, Neil D Young, Bronwyn E Campbell, Ross S Hall, Maria A Doyle, Stuart A Ralph, Elida M Rabelo, Shoba Ranganathan, Paul W Sternberg, Alex Loukas, Robin B Gasser

“…Comparative analyses of the transcriptomes of N. americanus and the canine hookworm, Ancylostoma caninum, revealed qualitative and quantitative differences. …”
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Transcriptomic insights into Candida albicans adaptation to an anaerobic environment by Karen D. Zeise, John R. Erb-Downward, Gary B. Huffnagle

“…In this study, we used RNA sequencing to compare the global transcriptomic profiles of two strains of C. albicans (SC5314 and CHN1) grown purely anaerobically to those grown aerobically. …”
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Validation of Immune Cell Modules in Multicellular Transcriptomic Data. by Gabriele Pollara, Matthew J Murray, James M Heather, Rachel Byng-Maddick, Naomi Guppy, Matthew Ellis, Carolin T Turner, Benjamin M Chain, Mahdad Noursadeghi

“…Numerous gene signatures, or modules have been described to evaluate the immune cell composition in transcriptomes of multicellular tissue samples. However, significant diversity in module gene content for specific cell types is associated with heterogeneity in their performance. …”
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Experiences of discrimination are associated with microbiome and transcriptome alterations in the gut by Tien S. Dong, Tien S. Dong, Tien S. Dong, Tien S. Dong, Tien S. Dong, Simer Shera, Kirstin Peters, Gilbert C. Gee, Gilbert C. Gee, Hiram Beltrán-Sánchez, Hiram Beltrán-Sánchez, May C. Wang, Lisa A. Kilpatrick, Lisa A. Kilpatrick, Lisa A. Kilpatrick, Lisa A. Kilpatrick, Xiaobei Zhang, Xiaobei Zhang, Xiaobei Zhang, Xiaobei Zhang, Xiaobei Zhang, Jennifer S. Labus, Jennifer S. Labus, Jennifer S. Labus, Jennifer S. Labus, Jennifer S. Labus, Allison Vaughan, Allison Vaughan, Allison Vaughan, Allison Vaughan, Arpana Church, Arpana Church, Arpana Church, Arpana Church, Arpana Church

“…This study explored the impact of discrimination on gut health, specifically focusing on microbiome changes, predicted metagenomic differences, transcriptomic profiles, and the potential for using a multi-omic approach to predict discrimination to identify discrimination status for an individual. …”
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Circadian regulation of the transcriptome in a complex polyploid crop. by Hannah Rees, Rachel Rusholme-Pilcher, Paul Bailey, Joshua Colmer, Benjamen White, Connor Reynolds, Sabrina Jaye Ward, Benedict Coombes, Calum A Graham, Luíza Lane de Barros Dantas, Antony N Dodd, Anthony Hall

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Integration of the transcriptome and glycome for identification of glycan cell signatures. by Sandra V Bennun, Kevin J Yarema, Michael J Betenbaugh, Frederick J Krambeck

“…To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. …”
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The endometrial transcriptome of infertile women with and without implantation failure by Bich Ngoc Bui, Viktorija Kukushkina, Alvin Meltsov, Catharina Olsen, Nienke vanHoogenhuijze, Signe Altmäe, Femke Mol, Gijs Teklenburg, Jan‐Peter deBruin, Dagmar Besselink, Linda Stevens Brentjens, Darina Obukhova, Masoud Zamani Esteki, Ron vanGolde, Andrea Romano, Triin Laisk, Gaby Steba, Shari Mackens, Andres Salumets, Frank Broekmans

“…However, there may be differences in the extent to which the WOI is displaced.…”
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Nuclear RNA sequencing of the mouse erythroid cell transcriptome. by Jennifer A Mitchell, Ieuan Clay, David Umlauf, Chih-Yu Chen, Catherine A Moir, Christopher H Eskiw, Stefan Schoenfelder, Lyubomira Chakalova, Takashi Nagano, Peter Fraser

“…In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. …”
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Comparative transcriptome analysis of bull X- and Y-spermatozoa by Sofi Imran Ul Umar, Sushil Prasad, Soumen Naskar, Pranab Jyoti Das, Mridula Sharma, Arunava Pattanayak, Dhanu Kumar Murasing, Vijai Pal Bhadana, Sujay Rakshit

“…However, lack of a comprehensive omics dataset hinders the ability to comprehend significant differences between the sorted sperm types and develop robust biomarkers for sex-sorting. …”
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Comparative transcriptomics of salinomycin molecular toxicity in chicken and turkey by İlksen Berfin Ekinci, Anna Sławińska, Kacper Żukowski, Małgorzata Olejnik

“…This comparative transcriptomic study aimed to determine molecular toxicity mechanisms of Sal in both species. …”
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Transcriptome-wide assessment of human brain and lymphocyte senescence. by Mun-Gwan Hong, Amanda J Myers, Patrik K E Magnusson, Jonathan A Prince

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Urine transcriptomic profile in terms of malignant ovarian tumors by D. S. Kutilin, F. E. Filippov, N. V. Porhanova, A. Yu. Maksimov

“…Bioinformatic search for transcriptomic markers (based on metabolomic data) and their validation in the urine of serous ovarian adenocarcinoma patients.Materials and methods. …”
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Full-Length Transcriptome Sequencing and Comparative Transcriptomics Reveal the Molecular Mechanisms Underlying Gonadal Development in Sleepy Cod (<i>Oxyeleotris lineolata</i>) by Jiajia Fan, Dongmei Ma, Huaping Zhu, Minghui Lin, Zaixuan Zhong, Yuanyuan Tian

“…Using the full-length transcriptome as a reference, short-read Illumina sequencing was performed to investigate the differences in gene expression at the transcriptome level between ovaries and testes from 12-month-old individuals. …”
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Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity by Yibo Jiang, Wen Li, Lihao Zheng, Nan Ba

“…Subsequently, machine learning algorithms were implemented via 10-fold cross-validation to build transcriptomic classifiers for MDD in a sex-stratified manner. …”
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Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment by Julie Vinkel, Alfonso Buil, Ole Hyldegaard

“…Gene expression profiles were analysed using machine learning techniques to identify sepsis endotypes, treatment response endotypes and clinically relevant transcriptomic signatures of response to treatment. Results We identified differences in gene expression profiles at follow-up between HBO2-treated patients and patients not treated with HBO2. …”
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Transcriptome Classification Reveals Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Yuxia Liu, Chang Xu, Wenxin Gao, Huaqiong Liu, Chenglong Li, Mingwei Chen

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Transcriptomics, Proteomics and Bioinformatics in Atrial Fibrillation: A Descriptive Review by Martina Belfiori, Lisa Lazzari, Melanie Hezzell, Gianni D. Angelini, Tim Dong

“…The objective of this descriptive review is to examine the most recent developments of transcriptomics, proteomics, and bioinformatics in atrial fibrillation.…”
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Heterogeneity and longitudinal transcriptomic characteristics of Tregs in COVID-19 patients by Yanling Wen, Juanjuan Zhao, Zheng Zhang

“…Using transcriptomic analysis, we evaluated the proportion and functionality of different Treg subsets, specifically HLA_DR+ Tregs, across different stages of COVID-19 patients.ResultsOur analysis revealed that the proportion of CCR7+ Tregs decreased as the disease advanced, while the cell proportion of HLA_DR+ regs escalated with the severity of the disease. …”
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