#transcriptomics — Public Fediverse posts

DATE: August 11, 2026 at 06:00AM
SOURCE: PSYPOST.ORG

** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------

TITLE: Cognitive abilities help explain regional brain aging patterns in anxiety and depression

URL: https://www.psypost.org/cognitive-abilities-help-explain-regional-brain-aging-patterns-in-anxiety-and-depression/

A new study published in the Journal of Affective Disorders suggests that the advanced brain aging often seen in people with anxiety and depression is partly associated with variations in their cognitive performance. By accounting for cognitive skills like memory and processing speed, scientists observed that the apparent effect of these psychiatric conditions on brain aging decreased by roughly twenty to twenty-five percent. This indicates that cognitive differences play an important role in understanding brain health in individuals with neuropsychiatric conditions.

Biological aging of the brain can sometimes diverge from chronological aging. Using structural magnetic resonance imaging, machine learning algorithms can predict a person’s brain age by comparing their brain structure to a large dataset of healthy individuals. The difference between this predicted age and the person’s actual age is called the brain age gap. A positive gap indicates an older-appearing brain, which is linked to cognitive decline, health risks, and various neurological conditions.

Neuropsychiatric disorders like depression and anxiety are associated with increased brain age gaps. People with these conditions also frequently experience cognitive difficulties that affect their attention, executive function, and processing speed. Executive function refers to a set of mental skills that include working memory, flexible thinking, and self-control. Because cognitive decline is a common feature of mood disorders, it is often difficult to tell if brain aging differences reflect the psychiatric diagnosis itself or the accompanying cognitive variation.

Previous neuroimaging research typically looked at global brain age, which assumes aging happens uniformly across the entire brain. This approach can obscure specific regional effects.

“Our main motivation was that most previous brain-age studies summarize the entire brain using a single number,” said Owen M. Vega, a doctoral candidate in the neuroscience graduate program at the University of Southern California and a researcher at the Ethel Percy Andrus Gerontology Center in the Leonard Davis School of Gerontology.

“While useful, that approach assumes the brain ages uniformly and provides little insight into why certain disorders are associated with advanced brain aging. We wanted to move beyond prediction toward biological understanding,” Vega told PsyPost.

By mapping brain age at a regional level and accounting for cognitive performance, the researchers aimed to identify the specific neural systems affected and link them to underlying cellular processes.

“Ultimately, this brings us closer to understanding the biological mechanisms that contribute to psychiatric brain aging rather than simply measuring that it exists,” Vega explained.

The authors analyzed data from 21,424 older adult participants in the UK Biobank. Participants were classified into four mutually exclusive groups based on their diagnostic status. The sample included 12,285 individuals with no psychiatric diagnosis, 1,746 with anxiety only, 4,267 with depression only, and 1,563 with comorbid anxiety and depression. Comorbidity means the individual met the criteria for both conditions.

To estimate regional brain ages, the scientists processed structural brain scans through a deep neural network, breaking the brain down into 187 distinct cortical and subcortical regions. Participants also completed several cognitive assessments measuring fluid intelligence, reaction time, and symbol substitution. These test results were statistically combined into a single principal component score representing general cognitive performance. The models also controlled for participant sex, years of education, and socioeconomic deprivation to isolate the variables of interest.

The researchers first ran a statistical model that did not account for cognitive performance. They found widespread regional brain age gap elevations across the psychiatric groups compared to the diagnosis-free participants. On average, brains in the anxiety group appeared about 1.01 years older than chronological age. Brains in the depression group appeared 1.05 years older, and brains in the comorbid group appeared 1.14 years older.

These elevated brain ages were widely distributed but particularly pronounced in specific areas. The largest gaps were observed in the anterior frontal and orbitofrontal regions, as well as the temporal pole. These areas are heavily involved in emotion regulation and reward processing.

Vega noted that while the overall increases are relatively small, they provide a starting point for exploring the biology of mental health.

“The effects are statistically robust but modest in size, with average differences of about one year. However, they should not be interpreted as the whole story,” Vega told PsyPost. “Averaging across the entire brain masks much larger regional differences. The real significance of this work lies in identifying where these changes occur.”

By pinpointing these spatial patterns, scientists can relate them to specific genes and molecular pathways, moving the field past simple summary measures.

“This moves brain-age research beyond a single summary measure toward understanding the mechanisms that may contribute to psychiatric illness and cognitive vulnerability,” Vega added.

Next, the authors ran a second model that included the participants’ general cognitive performance scores. Factoring in cognition reduced the magnitude of the brain age gaps by approximately twenty to twenty-five percent. The mean gap dropped to 0.80 years for the anxiety group, 0.84 years for the depression group, and 0.78 years for the comorbid group. Despite this reduction, the effects remained present, indicating that diagnostic status contributes to brain aging independent of cognitive ability.

“The main takeaway is that anxiety and depression are associated with subtle but measurable differences in how the brain ages, and those differences are not spread evenly across the brain,” Vega said.

By showing how these estimates change when mental skills are factored into the equations, the study refines how scientists understand brain health in clinical populations.

“We also found that part of the observed brain-age signal is associated with cognitive performance, showing that cognition is an important piece of the picture,” Vega explained. “More broadly, our work suggests that brain aging in psychiatric disorders reflects specific biological patterns rather than a single, uniform process, which may ultimately help researchers develop more biologically meaningful biomarkers.”

Higher cognitive performance was associated with a younger-looking brain, suggesting a protective effect. This association was noticeably stronger in all three psychiatric groups compared to the diagnosis-free participants. Interestingly, the brain regions most strongly associated with cognitive performance differed from the regions most affected by the psychiatric diagnoses.

Cognitive associations were strongest in subcortical and ventral regions of the brain. These included the thalamus, pallidum, and hippocampus, which are structures located deep beneath the cerebral cortex that are essential for memory formation and information integration. This dissociation suggests that psychiatric status and cognition exert distinct but overlapping influences on different neural systems.

The researchers also looked beyond the magnetic resonance imaging scans to see if their regional brain age maps aligned with other biological data, such as transcriptomics. Transcriptomics is the study of RNA molecules in cells, which reveals how specific genes are turned on or off to drive cellular activity.

“One of the most striking findings was that several independent biological analyses converged on the same underlying systems,” Vega said. “Regional brain-aging patterns identified from MRI aligned with transcriptomic enrichment and biological pathways in a remarkably consistent way.”

This overlap suggests that the structural differences visible on brain scans are directly tied to cellular and genetic changes.

“That convergence gives us greater confidence that these patterns reflect meaningful biology rather than isolated statistical findings, and suggests that regional brain age can serve as a bridge between neuroimaging and molecular neuroscience,” Vega added.

The cross-sectional design of the study relies on data collected at a single point in time. This prevents researchers from establishing the sequence of events.

“A key caveat is that these results are not causal. Our findings do not demonstrate that anxiety or depression directly accelerate brain aging,” Vega said. “Instead, they identify patterns of brain-aging vulnerability associated with psychiatric illness and cognitive performance.”

Tracking individuals over multiple years is necessary to determine if cognitive differences precede advanced brain aging or reflect the downstream consequences of an aging brain. Bidirectional influences are highly likely in these conditions.

“Longitudinal studies will be needed to determine how these relationships evolve over time and whether they predict future cognitive decline,” Vega explained. “The goal was to refine the interpretation of previous brain-age findings and pave the way to clinical research, not to claim a direct mechanism.”

The diagnostic classifications were derived from a combination of self-reported surveys and clinician-confirmed records. The available data lacked details regarding symptom severity, illness duration, and the age of onset. The researchers were unable to determine if the older brain ages were linked to more severe, chronic, or recurrent forms of mental illness. Residual misclassification or reporting bias might also introduce variability into the data.

The UK Biobank predominantly consists of White European participants who are often healthier than the general population. This demographic makeup limits how well these findings apply to more diverse groups worldwide. Environmental factors, cultural differences, and early-life stressors that influence brain aging were not fully captured in the dataset. Future research should prioritize replicating these findings in more ethnically diverse cohorts.

Future research will continue to explore the genetic and molecular factors that drive these localized brain changes.

“Our next step is to relate regional brain-age maps to other spatially organized biological data,” Vega said. “We are now integrating regional brain-age maps with transcriptomic, genetic, and cellular datasets to identify the biological pathways associated with vulnerability to psychiatric brain aging.”

By building a more comprehensive biological profile, the team aims to improve risk assessments for aging adults.

“Ultimately, we hope this work will improve biologically informed risk stratification, help identify individuals at greatest risk for later cognitive decline, and reveal biological systems that may become targets for future therapeutic interventions,” Vega concluded.

The study, “Cognitive performance modulates regional brain age differences in clinical anxiety and depression,” was authored by Owen M. Vega, Phoebe Imms, Nikhil N. Chaudhari, Wendy J. Mack, Nahian F. Chowdhury, and Andrei Irimia.

URL: https://www.psypost.org/cognitive-abilities-help-explain-regional-brain-aging-patterns-in-anxiety-and-depression/

-------------------------------------------------

Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org

Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

-------------------------------------------------

#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainAging #AnxietyDepression #CognitivePerformance #RegionalBrainAge #Neuroimaging #MentalHealthBiology #BrainAgeGap #CognitionAndBrain #Transcriptomics #BiomarkersInMentalHealth

DATE: August 11, 2026 at 06:00AM
SOURCE: PSYPOST.ORG

** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------

TITLE: Cognitive abilities help explain regional brain aging patterns in anxiety and depression

URL: https://www.psypost.org/cognitive-abilities-help-explain-regional-brain-aging-patterns-in-anxiety-and-depression/

A new study published in the Journal of Affective Disorders suggests that the advanced brain aging often seen in people with anxiety and depression is partly associated with variations in their cognitive performance. By accounting for cognitive skills like memory and processing speed, scientists observed that the apparent effect of these psychiatric conditions on brain aging decreased by roughly twenty to twenty-five percent. This indicates that cognitive differences play an important role in understanding brain health in individuals with neuropsychiatric conditions.

Biological aging of the brain can sometimes diverge from chronological aging. Using structural magnetic resonance imaging, machine learning algorithms can predict a person’s brain age by comparing their brain structure to a large dataset of healthy individuals. The difference between this predicted age and the person’s actual age is called the brain age gap. A positive gap indicates an older-appearing brain, which is linked to cognitive decline, health risks, and various neurological conditions.

Neuropsychiatric disorders like depression and anxiety are associated with increased brain age gaps. People with these conditions also frequently experience cognitive difficulties that affect their attention, executive function, and processing speed. Executive function refers to a set of mental skills that include working memory, flexible thinking, and self-control. Because cognitive decline is a common feature of mood disorders, it is often difficult to tell if brain aging differences reflect the psychiatric diagnosis itself or the accompanying cognitive variation.

Previous neuroimaging research typically looked at global brain age, which assumes aging happens uniformly across the entire brain. This approach can obscure specific regional effects.

“Our main motivation was that most previous brain-age studies summarize the entire brain using a single number,” said Owen M. Vega, a doctoral candidate in the neuroscience graduate program at the University of Southern California and a researcher at the Ethel Percy Andrus Gerontology Center in the Leonard Davis School of Gerontology.

“While useful, that approach assumes the brain ages uniformly and provides little insight into why certain disorders are associated with advanced brain aging. We wanted to move beyond prediction toward biological understanding,” Vega told PsyPost.

By mapping brain age at a regional level and accounting for cognitive performance, the researchers aimed to identify the specific neural systems affected and link them to underlying cellular processes.

“Ultimately, this brings us closer to understanding the biological mechanisms that contribute to psychiatric brain aging rather than simply measuring that it exists,” Vega explained.

The authors analyzed data from 21,424 older adult participants in the UK Biobank. Participants were classified into four mutually exclusive groups based on their diagnostic status. The sample included 12,285 individuals with no psychiatric diagnosis, 1,746 with anxiety only, 4,267 with depression only, and 1,563 with comorbid anxiety and depression. Comorbidity means the individual met the criteria for both conditions.

To estimate regional brain ages, the scientists processed structural brain scans through a deep neural network, breaking the brain down into 187 distinct cortical and subcortical regions. Participants also completed several cognitive assessments measuring fluid intelligence, reaction time, and symbol substitution. These test results were statistically combined into a single principal component score representing general cognitive performance. The models also controlled for participant sex, years of education, and socioeconomic deprivation to isolate the variables of interest.

The researchers first ran a statistical model that did not account for cognitive performance. They found widespread regional brain age gap elevations across the psychiatric groups compared to the diagnosis-free participants. On average, brains in the anxiety group appeared about 1.01 years older than chronological age. Brains in the depression group appeared 1.05 years older, and brains in the comorbid group appeared 1.14 years older.

These elevated brain ages were widely distributed but particularly pronounced in specific areas. The largest gaps were observed in the anterior frontal and orbitofrontal regions, as well as the temporal pole. These areas are heavily involved in emotion regulation and reward processing.

Vega noted that while the overall increases are relatively small, they provide a starting point for exploring the biology of mental health.

“The effects are statistically robust but modest in size, with average differences of about one year. However, they should not be interpreted as the whole story,” Vega told PsyPost. “Averaging across the entire brain masks much larger regional differences. The real significance of this work lies in identifying where these changes occur.”

By pinpointing these spatial patterns, scientists can relate them to specific genes and molecular pathways, moving the field past simple summary measures.

“This moves brain-age research beyond a single summary measure toward understanding the mechanisms that may contribute to psychiatric illness and cognitive vulnerability,” Vega added.

Next, the authors ran a second model that included the participants’ general cognitive performance scores. Factoring in cognition reduced the magnitude of the brain age gaps by approximately twenty to twenty-five percent. The mean gap dropped to 0.80 years for the anxiety group, 0.84 years for the depression group, and 0.78 years for the comorbid group. Despite this reduction, the effects remained present, indicating that diagnostic status contributes to brain aging independent of cognitive ability.

“The main takeaway is that anxiety and depression are associated with subtle but measurable differences in how the brain ages, and those differences are not spread evenly across the brain,” Vega said.

By showing how these estimates change when mental skills are factored into the equations, the study refines how scientists understand brain health in clinical populations.

“We also found that part of the observed brain-age signal is associated with cognitive performance, showing that cognition is an important piece of the picture,” Vega explained. “More broadly, our work suggests that brain aging in psychiatric disorders reflects specific biological patterns rather than a single, uniform process, which may ultimately help researchers develop more biologically meaningful biomarkers.”

Higher cognitive performance was associated with a younger-looking brain, suggesting a protective effect. This association was noticeably stronger in all three psychiatric groups compared to the diagnosis-free participants. Interestingly, the brain regions most strongly associated with cognitive performance differed from the regions most affected by the psychiatric diagnoses.

Cognitive associations were strongest in subcortical and ventral regions of the brain. These included the thalamus, pallidum, and hippocampus, which are structures located deep beneath the cerebral cortex that are essential for memory formation and information integration. This dissociation suggests that psychiatric status and cognition exert distinct but overlapping influences on different neural systems.

The researchers also looked beyond the magnetic resonance imaging scans to see if their regional brain age maps aligned with other biological data, such as transcriptomics. Transcriptomics is the study of RNA molecules in cells, which reveals how specific genes are turned on or off to drive cellular activity.

“One of the most striking findings was that several independent biological analyses converged on the same underlying systems,” Vega said. “Regional brain-aging patterns identified from MRI aligned with transcriptomic enrichment and biological pathways in a remarkably consistent way.”

This overlap suggests that the structural differences visible on brain scans are directly tied to cellular and genetic changes.

“That convergence gives us greater confidence that these patterns reflect meaningful biology rather than isolated statistical findings, and suggests that regional brain age can serve as a bridge between neuroimaging and molecular neuroscience,” Vega added.

The cross-sectional design of the study relies on data collected at a single point in time. This prevents researchers from establishing the sequence of events.

“A key caveat is that these results are not causal. Our findings do not demonstrate that anxiety or depression directly accelerate brain aging,” Vega said. “Instead, they identify patterns of brain-aging vulnerability associated with psychiatric illness and cognitive performance.”

Tracking individuals over multiple years is necessary to determine if cognitive differences precede advanced brain aging or reflect the downstream consequences of an aging brain. Bidirectional influences are highly likely in these conditions.

“Longitudinal studies will be needed to determine how these relationships evolve over time and whether they predict future cognitive decline,” Vega explained. “The goal was to refine the interpretation of previous brain-age findings and pave the way to clinical research, not to claim a direct mechanism.”

The diagnostic classifications were derived from a combination of self-reported surveys and clinician-confirmed records. The available data lacked details regarding symptom severity, illness duration, and the age of onset. The researchers were unable to determine if the older brain ages were linked to more severe, chronic, or recurrent forms of mental illness. Residual misclassification or reporting bias might also introduce variability into the data.

The UK Biobank predominantly consists of White European participants who are often healthier than the general population. This demographic makeup limits how well these findings apply to more diverse groups worldwide. Environmental factors, cultural differences, and early-life stressors that influence brain aging were not fully captured in the dataset. Future research should prioritize replicating these findings in more ethnically diverse cohorts.

Future research will continue to explore the genetic and molecular factors that drive these localized brain changes.

“Our next step is to relate regional brain-age maps to other spatially organized biological data,” Vega said. “We are now integrating regional brain-age maps with transcriptomic, genetic, and cellular datasets to identify the biological pathways associated with vulnerability to psychiatric brain aging.”

By building a more comprehensive biological profile, the team aims to improve risk assessments for aging adults.

“Ultimately, we hope this work will improve biologically informed risk stratification, help identify individuals at greatest risk for later cognitive decline, and reveal biological systems that may become targets for future therapeutic interventions,” Vega concluded.

The study, “Cognitive performance modulates regional brain age differences in clinical anxiety and depression,” was authored by Owen M. Vega, Phoebe Imms, Nikhil N. Chaudhari, Wendy J. Mack, Nahian F. Chowdhury, and Andrei Irimia.

URL: https://www.psypost.org/cognitive-abilities-help-explain-regional-brain-aging-patterns-in-anxiety-and-depression/

-------------------------------------------------

Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org

Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

-------------------------------------------------

#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainAging #AnxietyDepression #CognitivePerformance #RegionalBrainAge #Neuroimaging #MentalHealthBiology #BrainAgeGap #CognitionAndBrain #Transcriptomics #BiomarkersInMentalHealth

Pipeline release! nf-core/spatialaxe v1.0.1 - 1.0.1!
A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data
Please see the changelog: https://github.com/nf-core/spatialaxe/releases/tag/1.0.1

#10xgenomics #atera #imageprocessing #spatial #spatialdataanalysis #spatialtranscriptomics #transcriptomics #xenium #nfcore #openscience #nextflow #bioinformatics

Pipeline release! nf-core/spatialaxe v1.0.1 - 1.0.1!
A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data
Please see the changelog: https://github.com/nf-core/spatialaxe/releases/tag/1.0.1

#10xgenomics #atera #imageprocessing #spatial #spatialdataanalysis #spatialtranscriptomics #transcriptomics #xenium #nfcore #openscience #nextflow #bioinformatics

Breaking the Reference Trap: How RETROFIT Decodes Tissue Architecture Without a Single-Cell Map

Follow us and never miss a story.

https://1ban.news/retrofit-spatial-transcriptomics/

#1ban #retrofit #spatial #transcriptomics #science #research

Breaking the Reference Trap: How RETROFIT Decodes Tissue Architecture Without a Single-Cell Map

Follow us and never miss a story.

https://1ban.news/retrofit-spatial-transcriptomics/

#1ban #retrofit #spatial #transcriptomics #science #research

Breaking the Reference Trap: How RETROFIT Decodes Tissue Architecture Without a Single-Cell Map

Repost to help others discover this.

https://1ban.news/retrofit-spatial-transcriptomics-reference-free/

#1ban #retrofit #spatial #transcriptomics #reference #science

Breaking the Reference Trap: How RETROFIT Decodes Tissue Architecture Without a Single-Cell Map

Repost to help others discover this.

https://1ban.news/retrofit-spatial-transcriptomics-reference-free/

#1ban #retrofit #spatial #transcriptomics #reference #science

Changes in ppGpp levels impact gene expression and virulence features of Adherent-Invasive Escherichia coli strain LF82

https://davidojcius.blogspot.com/2026/07/changes-in-ppgpp-levels-impact-gene.html

#OpenAccess #transcriptomics #virulence #microbiology

Changes in ppGpp levels impact gene expression and virulence features of Adherent-Invasive Escherichia coli strain LF82

https://davidojcius.blogspot.com/2026/07/changes-in-ppgpp-levels-impact-gene.html

#OpenAccess #transcriptomics #virulence #microbiology

For biosciences researchers in the #UK interested in #FAIR and #OpenScience

BioFAIR Fellowship Programme - BioFAIR - https://biofair.uk/biofair-fellowship-programme-2/

#BioFAIR #UKRI #Funding #FAIR #Biosciences #Neuroscience #Genomics #Transcriptomics

For biosciences researchers in the #UK interested in #FAIR and #OpenScience

BioFAIR Fellowship Programme - BioFAIR - https://biofair.uk/biofair-fellowship-programme-2/

#BioFAIR #UKRI #Funding #FAIR #Biosciences #Neuroscience #Genomics #Transcriptomics

Pipeline release! nf-core/spatialaxe v1.0.0 - 1.0.0!
A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data
Please see the changelog: https://github.com/nf-core/spatialaxe/releases/tag/1.0.0

#10xgenomics #atera #imageprocessing #spatial #spatialdataanalysis #spatialtranscriptomics #transcriptomics #xenium #nfcore #openscience #nextflow #bioinformatics

Pipeline release! nf-core/spatialaxe v1.0.0 - 1.0.0!
A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data
Please see the changelog: https://github.com/nf-core/spatialaxe/releases/tag/1.0.0

#10xgenomics #atera #imageprocessing #spatial #spatialdataanalysis #spatialtranscriptomics #transcriptomics #xenium #nfcore #openscience #nextflow #bioinformatics

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371

Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

Curious about the dark side of plant biology?

Check out our latest preprint on the secrets of black pigmentation in Rubus:

https://doi.org/10.64898/2026.05.05.723051

#Anthocyanins #Transcriptomics #PlantScience #Fruits
@PuckerLab

Curious about the dark side of plant biology?

Check out our latest preprint on the secrets of black pigmentation in Rubus:

https://doi.org/10.64898/2026.05.05.723051

#Anthocyanins #Transcriptomics #PlantScience #Fruits
@PuckerLab

🧬 Could a single metric decode how genes are regulated across cells?

🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0044

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

#GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

🧬 Could a single metric decode how genes are regulated across cells?

🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0044

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

#GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0052

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

#RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0052

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

#RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

"Cellular morphology emerges from polygenic, distributed transcriptional variation", Paylakhi et al. 2026
https://www.biorxiv.org/content/10.64898/2026.03.12.711281v1

#CellBiology #transcriptomics #CellPainting #RNAseq

"Cellular morphology emerges from polygenic, distributed transcriptional variation", Paylakhi et al. 2026
https://www.biorxiv.org/content/10.64898/2026.03.12.711281v1

#CellBiology #transcriptomics #CellPainting #RNAseq

At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

Is spatial transcriptomics data preprocessing giving you a headache? You're not alone! 🤯
Join our webinar designed for new researchers to simplify the complex world of image-based spatial omics. We'll walk through the practical workflow step-by-step, from initial cell segmentation to cleaning up your data.
Get the foundational skills you need. 📅 Sign up here: https://t1p.de/lqbg1

#Transcriptomics #SpatialTranscriptomics #Omics #DataScience #ResearchWebinar #GHGA #ELIXIR #deNBI

Research Associate
University of Ottawa

See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/

#cellbiology #molecularbiology #transcriptomics #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/?fsp_sid=4260

Research Associate
University of Ottawa

See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/

#cellbiology #molecularbiology #transcriptomics #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/?fsp_sid=4260

Our review article got published in 'Biotechnology Advances':

"Decoding bioprocesses with transcriptomics: current status and future potential"
https://doi.org/10.1016/j.biotechadv.2025.108736

#Biotech #ScaleUp #ScaleDown #Transcriptomics
@PuckerLab

Our review article got published in 'Biotechnology Advances':

"Decoding bioprocesses with transcriptomics: current status and future potential"
https://doi.org/10.1016/j.biotechadv.2025.108736

#Biotech #ScaleUp #ScaleDown #Transcriptomics
@PuckerLab

The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/

@galaxyfreiburg
#UseGalaxy #GalaxyProject #EOSC #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch #GalaxyCommunity #SPOC (1/2)

The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/

@galaxyfreiburg
#UseGalaxy #GalaxyProject #EOSC #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch #GalaxyCommunity #SPOC (1/2)

The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/

@galaxyproject
#UseGalaxy #GalaxyProject #EOSC #UniFreiburg #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch (1/2)

The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/

@galaxyproject
#UseGalaxy #GalaxyProject #EOSC #UniFreiburg #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch (1/2)

🧬 Can a single method balance sensitivity and specificity in small RNA annotation across the tree of life?

🔗 Class-agnostic annotation of small RNAs balances sensitivity and specificity in diverse organisms. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.05.045

📚 CSBJ: https://www.csbj.org/

#RNA #Bioinformatics #Genomics #SmallRNAs #ComputationalBiology #Transcriptomics #MolecularBiology #Epigenetics

🧬 Can a single method balance sensitivity and specificity in small RNA annotation across the tree of life?

🔗 Class-agnostic annotation of small RNAs balances sensitivity and specificity in diverse organisms. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.05.045

📚 CSBJ: https://www.csbj.org/

#RNA #Bioinformatics #Genomics #SmallRNAs #ComputationalBiology #Transcriptomics #MolecularBiology #Epigenetics

New preprint, with a modest contribution from yours truly, on proteo-transcriptomic changes in the aging mammalian brain.

https://www.biorxiv.org/content/10.1101/2025.08.14.669896v1

#SystemsBiology #Proteomics #Transcriptomics #neuroscience

Postdoc Position: Evolution and Organization of Ant Olfactory Systems
Johannes Gutenberg University Mainz

PostDoc position @uni Mainz (Germany)
#genomics #transcriptomics to investigate the olfactory system of ants

See the full job description on jobRxiv: https://jobrxiv.org/job/johannes-gutenberg-university-mainz-27778-postdoc-position-evolu...
https://jobrxiv.org/job/johannes-gutenberg-university-mainz-27778-postdoc-position-evolution-and-organization-of-ant-olfactory-systems/?fsp_sid=550

Postdoc Position: Evolution and Organization of Ant Olfactory Systems
Johannes Gutenberg University Mainz

PostDoc position @uni Mainz (Germany)
#genomics #transcriptomics to investigate the olfactory system of ants

See the full job description on jobRxiv: https://jobrxiv.org/job/johannes-gutenberg-university-mainz-27778-postdoc-position-evolu...
https://jobrxiv.org/job/johannes-gutenberg-university-mainz-27778-postdoc-position-evolution-and-organization-of-ant-olfactory-systems/?fsp_sid=550

My son, Ronan, who is double-majoring in #biochemistry and #physics, is working at Georgetown University in DC this summer, on a #cancer research internship. His work focuses on #cell-type #deconvolution in spatial #transcriptomics.

I know nothing about biology, but I am assisting him with deconvolution. My MathsTodon friends, have you any guidance to offer, either in mathematics or in biology?

My son, Ronan, who is double-majoring in #biochemistry and #physics, is working at Georgetown University in DC this summer, on a #cancer research internship. His work focuses on #cell-type #deconvolution in spatial #transcriptomics.

I know nothing about biology, but I am assisting him with deconvolution. My MathsTodon friends, have you any guidance to offer, either in mathematics or in biology?

🧬 Can advanced sequencing technologies bring clarity to genetic variants once deemed uncertain?

🔗 Combining long-read DNA and RNA sequencing to enhance molecular understanding of structural variations leading to copy gains. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.04.031

📚 CSBJ: https://www.csbj.org/

#Genomics #StructuralVariants #LongReadSequencing #RareDisease #PrecisionMedicine #RNAseq #Nanopore #GeneticDiagnostics #NanoporeSequencing #Transcriptomics

"Scalable image-based visualization and alignment of spatial transcriptomics datasets", by Preibisch et al. 2025 @preibischs
https://www.sciencedirect.com/science/article/pii/S2405471225000973
#transcriptomics #BioimageInformatics

"Scalable image-based visualization and alignment of spatial transcriptomics datasets", by Preibisch et al. 2025 @preibischs
https://www.sciencedirect.com/science/article/pii/S2405471225000973
#transcriptomics #BioimageInformatics

Landscape #transcriptomics may give insight into what #stresses wild #bees.

#RNA #conservation #AI #genomics #conservation #bumblebees #Bombus #funded_by_USDA #funded_by_us

https://phys.org/news/2025-03-landscape-transcriptomics-insight-stresses-wild.html

Landscape #transcriptomics may give insight into what #stresses wild #bees.

#RNA #conservation #AI #genomics #conservation #bumblebees #Bombus #funded_by_USDA #funded_by_us

https://phys.org/news/2025-03-landscape-transcriptomics-insight-stresses-wild.html

Last time teaching Applied Plant Transcriptomics at TU Braunschweig 🌱🧬
Slides & resources: https://github.com/bpucker/AppliedPlantTranscriptomics
#Bioinformatics #Transcriptomics
@PuckerLab

Last time teaching Applied Plant Transcriptomics at TU Braunschweig 🌱🧬
Slides & resources: https://github.com/bpucker/AppliedPlantTranscriptomics
#Bioinformatics #Transcriptomics
@PuckerLab

Pipeline release! nf-core/molkart v1.1.0 - 1.1.0 - Resolution Road!

Please see the changelog: https://github.com/nf-core/molkart/releases/tag/1.1.0

#fish #imageprocessing #imaging #molecularcartography #segmentation #singlecell #spatial #transcriptomics #nfcore #openscience #nextflow #bioinformatics

Pipeline release! nf-core/molkart v1.1.0 - 1.1.0 - Resolution Road!

Please see the changelog: https://github.com/nf-core/molkart/releases/tag/1.1.0

#fish #imageprocessing #imaging #molecularcartography #segmentation #singlecell #spatial #transcriptomics #nfcore #openscience #nextflow #bioinformatics

Key #genes for #corn #architecture revealed, identifying future #breeding targets.

#agriculture #food-security #genomics #gene_regulation #pleiotropy #transcription_factors #transcriptomics #funded_by_NSF #funded_by_us

https://phys.org/news/2025-03-key-genes-corn-architecture-revealed.html

Key #genes for #corn #architecture revealed, identifying future #breeding targets.

#agriculture #food-security #genomics #gene_regulation #pleiotropy #transcription_factors #transcriptomics #funded_by_NSF #funded_by_us

https://phys.org/news/2025-03-key-genes-corn-architecture-revealed.html

The #eye is famously a well-accessible extension of the brain. So why not looking into eyes to find out about brain disorders? For #schizophrenia this may become possible as retinal cells are affected. #genomics #transcriptomics https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2829092

A genetic barcoding system lets scientists tag and track cells, making it easier than ever to map gene expression (#transcriptomics) and decode behaviour.
https://elifesciences.org/reviewed-preprints/88334?utm_source=mastodon&utm_medium=social&utm_campaign=organic

new paper: "A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease" https://doi.org/10.1038/s41598-025-85580-4

"our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives."

#bioinformatics #huntingtons #transcriptomics #rareDisease