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Spatial Mapping of Genetic Liability to Psychiatric Disorders in the Adult Human Hippocampus.

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  1. [1] § Methods and Materials › ST Data ↔ index.Rmd, lines 19–88 · score 0.60 · Genomics Visium Spatial, Visium spots, Gene Expression, histologically, anterior, platform

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R Markdown · 114 lines · 9.6 KB · GPL-3.0 · 1 match

  1. ---
  2. title: "VistoSeg: Visium Histology Image Segmentation and Processing Pipeline"
  3. author:
  4. - name: Madhavi Tippani
  5. affiliation:
  6. - &libd Lieber Institute for Brain Development, Johns Hopkins Medical Campus
  7. email: [email hidden]
  8. site: bookdown::bookdown_site
  9. apple-touch-sin: "icon_192.png"
  10. apple-touch-icon-size: 192
  11. favicon: "icon_32.png"
  12. github-repo: "LieberInstitute/VistoSeg"
  13. documentclass: book
  14. output:
  15. bookdown::gitbook: default
  16. #bookdown::pdf_book: default
  17. ---
  19. # Overview {-}
  21. Histological imaging is a critical first step of the **spatial transcriptomics** workflow, a barcoding-based transcriptome-wide technology released by [10x Genomics](https://www.10xgenomics.com).
  22. <center>
  23. ![](images/visium_imaging.png)
  24. </center>
  25. <center>
  26. ![](images/visium_spot.png)
  27. </center>
  29. ## Why *spatial transcriptomics* or *Visium imaging*? {-}
  31. Methods like single-nucleus and single cell RNA-sequencing (RNA-seq) can profile single cells transcriptome-wide enabling researchers to identify cell type compositions; however, these methods necessarily destroy information about spatial positioning. On the other hand, multiplexing and in situ sequencing methods can provide spatial information, but have significant limitations on the number of genes that can be processed as well as issues with microscopy and related computational challenges. **Spatial transcriptomics**, including the 10X Genomics Visium platform, provides solutions to these limitations by allowing researchers to quantify gene expression with high spatial resolution. A critical component for the 10X Genomics platform to provide spatial context is the **Visium imaging** of the [Visium gene expression slide](https://www.10xgenomics.com/products/spatial-gene-expression). On this slide, the experimental tissue sections that are being analyzed are mounted onto the **capture areas (A1,B1,C1,D1)** located on the slide. The whole slide is then imaged, producing a large output image file that contains all of the capture areas. This image of the whole slide then has to be subsequently split into individual capture area images (necessarily JPEG or tif), which are then processed accordingly for the downstream gene expression analyses.
  33. This website describes the steps required to split, visualize and process the Visium images from spatial transcriptomics projects generated by the [10x Genomics Visium](https://www.10xgenomics.com/spatial-transcriptomics) commercial platform.
  34. \centering
  35. ![](images/VistoSeg.png)
  36. The above figure describes the **`VistoSeg` pipeline**, **(A)** The data presented here is from tissue sections obtained from three levels of the human dorsolateral prefrontal cortex (DLPFC; posterior, middle and anterior). Each tissue section spans the six cortical layers plus the white matter. **(B)** Shows the original *'Visium gene expression slide'* with 4 capture areas, and the slide scanner used to image the slide. **(C)** Shows the large tif file produced by the slide scanner, which is then split into the respective capture areas using the function [splitSlide](https://github.com/LieberInstitute/VistoSeg/blob/main/code/split.m) described in [Step 1](http://research.libd.org/VistoSeg/step-1-split-visium-histology-whole-slide-image-into-individual-capture-area-images.html). **(D)** Shows the individual tif images of capture areas produced by [splitSlide](https://github.com/LieberInstitute/VistoSeg/blob/main/code/split.m), and the corresponding nuclei segmentations produced by the functions [VNS](https://github.com/LieberInstitute/VistoSeg/blob/main/code/VNS.m) (Visium Nuclei Segmentation) and [refineVNS](https://github.com/LieberInstitute/VistoSeg/blob/main/code/refineVNS.m) explained in [Step 2](http://research.libd.org/VistoSeg/step-2-nuclei-segmentation-of-individual-capture-areas-images.html). **(E)** The tif images from *'(D)'* serve as input to the [Spaceranger](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/using/count) module (explained in [Step 3](https://research.libd.org/VistoSeg/step-3-spaceranger.html)), which generates [tissue_positions_list.csv](https://github.com/LieberInstitute/VistoSeg/blob/main/pipeline_outputs/spaceranger/tissue_positions_list.csv) file and [scalefactors_json.json](https://github.com/LieberInstitute/VistoSeg/blob/main/pipeline_outputs/spaceranger/scalefactors_json.json) file that contain *'Visium spot metrics'*. **(F)** The function [countNuclei](https://github.com/LieberInstitute/VistoSeg/blob/main/code/countNuclei.m) explained in [Step 4](https://research.libd.org/VistoSeg/step-4-gui-to-count-nuclei-in-a-visium-spot.html), gives the nuclei count per Visium spot info that is stored in [tissue_spot_counts.csv](https://github.com/LieberInstitute/VistoSeg/blob/main/pipeline_outputs/spotspotcheck/tissue_spot_counts.csv) file. **(G)** Finally, the pipeline provides a GUI called [spotspotcheck](https://research.libd.org/VistoSeg/step-4-gui-to-count-nuclei-in-a-visium-spot.html#spotspotcheck) that allows the user to perform visual inspection of the nuclei segmentations by allowing the user to toggle between the Visium and binary images, and also provides zoom in/out options to clearly identify cell bodies within a Visium spot.
  38. ## Cite `VistoSeg` {-}
  40. We hope that [`VistoSeg`](http://research.libd.org/VistoSeg/) will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
  41. ```
  42. @article {Tippani2022,
  43. author = {Tippani, Madhavi and Divecha, Heena R. and Catallini II,
  44. Joseph L. and Kwon, Sang Ho and Weber, Lukas M. and Spangler, Abby and Jaffe,
  45. Andrew E. and Hicks, Stephanie C. and Martinowich, Keri and
  46. Collado-Torres, Leonardo and Page, Stephanie C. and Maynard,
  47. Kristen R.},
  48. title = {VistoSeg: processing utilities for high-resolution Visium/Visium-IF
  49. images for spatial transcriptomics data},
  50. year = {2022},
  51. doi = {[https://doi.org/10.1101/2021.08.04.452489](https://doi.org/10.1101/2021.08.04.452489)},
  52. publisher = {TODO},
  53. URL = {TODO},
  54. journal = {bioRxiv}
  55. }
  56. ```
  57. Project lead: [Madhavi Tippani](https://twitter.com/MadhaviTippani), Staff Scientist in the **Imaging Development Group** at the [Lieber Institute for Brain Development](https://www.libd.org/).
  58. <center>
  59. <img src="http://lcolladotor.github.io/img/LIBD_logo.jpg" width="250px">
  60. </center>
  62. Initial versions of `spotspotcheck` and `countNuclei` were developed by [Joseph L. Catallini II](https://www.linkedin.com/in/joseph-catallini-ii/).
  64. ## Image Acquisition {-}
  66. The 10X [Visium Spatial Gene Expression Imaging Guidelines](https://support.10xgenomics.com/spatial-gene-expression/imaging/doc/technical-note-visium-spatial-gene-expression-imaging-guidelines) are followed for acquiring the images. Images are acquired at 40x magnification using a Leica CS2 slide scanner and saved as *'.SVS files.'* These *'.SVS files'* are then exported as *'TIF files'* for downstream analysis. The entire Visium slide (4 capture areas with fiducial frames), is scanned in a single file (~20GB).
  68. ## Software Requirements {-}
  70. The pipeline was developed under the following software configuration.
  71. <center>
  72. ![](images/Software_Configuration.png)
  73. </center>
  75. `VistoSeg` has been tested on Linux, Windows and MacOS.
  77. 1. `MATLAB`
  78. MATLAB version R2019a 64-bit or later is required to run the `VistoSeg` pipeline with the [Image Processing Toolbox](https://www.mathworks.com/products/image.html) preloaded.
  80. 2. `Memory`
  81. Visium whole slide images are high resolution, and the typical size of these multiplane tif images produced in-house is ~25GB. The system RAM (we use ~75GB) should be thrice the size of the multiplane tif image to load it into MATLAB and split them into individual capture areas. The rest of the processing, on individual capture tifs can be performed on a system with as little as 16GB of RAM.
  83. 3. `Installation`
  84. The pipeline is available at https://github.com/LieberInstitute/VistoSeg, which can be download to your system from the Github website directly or the main repository can be cloned to your system using the following command on terminal/command prompt.
  86. ```{bash, eval = FALSE}
  87. git clone https://github.com/LieberInstitute/VistoSeg.git
  88. ```
  90. All the code exists in the [code](https://github.com/LieberInstitute/VistoSeg/tree/main/code) directory inside the main [`VistoSeg`](https://github.com/LieberInstitute/VistoSeg) directory.
  91. The user's working directory on MATLAB should be the path to the [code](https://github.com/LieberInstitute/VistoSeg/tree/main/code) directory in the downloaded repository, to run any functions this pipeline provides. Once the repository is downloaded, the user can run either of the following code to change their working directory on MATLAB to the [code](https://github.com/LieberInstitute/VistoSeg/tree/main/code) directory.
  93. ```{bash, eval = FALSE}
  94. cd /path_to_the_downloaded_repository/VistoSeg/code/
  95. ```
  97. ```MATLAB
  98. addpath(genpath('/path_to_the_downloaded_repository/VistoSeg/code/'))
  99. ```
  101. ## Getting Help {-}
  103. If you are using `VistoSeg` and are running into unexpected problems, please [report them](https://github.com/LieberInstitute/VistoSeg/issues) publicly such that other users might benefit from the answers. Thank you!
  105. ## Data Availability {-}
  107. The raw Visium `.tif` file is available at through [AWS](https://visium-libd.s3.amazonaws.com/Lieber_Institute_OTS-20-7690_rush_anterior.tif).
  109. **Other potential datasets**
  110. 1. [LIBD pilot DLPFC](https://github.com/LieberInstitute/spatialLIBD#raw-data)
  111. 2. [10x Genomics spatial data sets](https://support.10xgenomics.com/spatial-gene-expression/datasets)
  112. 3. All input files and output files for the samples images used in every step in the pipeline are provided at the end of the section.

index.Rmd at commit cd8ca31, under GPL-3.0 · at the source

Overview

Authors: Yusuf Baran1,2, Darren Cameron1, Adele Pryce-Roberts3, Alexander L Richards1, Caleb Webber3, Michael C O’Donovan1, Nicholas J Bray1
ORCID iDs: Yusuf Baran, Darren Cameron, Nicholas J Bray
  1. Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
  2. Betul-Ziya Eren Genome and Stem Cell Center, Erciyes Üniversitesi, Kayseri, Türkiye
  3. UK Dementia Research Institute at Cardiff, Cardiff University, Cardiff, United Kingdom
Institutions: Cardiff University (United Kingdom); Erciyes University (Türkiye)
Journal: Biological psychiatry global open science, volume 6, issue 3, article 100719
Dates: received 2 December 2025; accepted 25 February 2026; published online 5 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bpsgos.2026.100719 · PMID 42016326 · PMCID PMC13094429 · OpenAlex W7133949219
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), depression (population), schizophrenia / psychosis (population), bipolar (population), cellular / molecular (subfield)
Methods: Statistics, Physiology & signal measures
Keywords: Bipolar disorder, Depression, Genetic variants, GWAS, Hippocampus, Schizophrenia, Spatial transcriptomics
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: UK Research and Innovation; Medical Research Council
Citations: cited by 1 paper (Europe PMC); 40 references in the paper

Abstract

Background: Common genetic variants associated with psychiatric disorders are enriched in genes with high expression specificity for hippocampal neurons. However, to date, these studies have been based on measures of gene expression from nuclei in liquid suspensions, where information on the precise location of cells within the assayed tissue is lost.

Methods: We applied genetically informed spatial mapping of cells for complex traits (gsMAP) to test enrichment of common-variant genetic liability to schizophrenia, bipolar disorder, and major depressive disorder (MDD) in 13 hippocampal subregions according to expression specificity of associated genes using spatial transcriptomic (ST) data from 10 neurotypical adult donors. Then we used cellular deconvolution data from ST spots to perform further gsMAP analyses on cell populations within enriched subregions.

Results: Compared with other hippocampal subregions, common-variant liability to schizophrenia, bipolar disorder, and, to a lesser extent, MDD, was significantly enriched in genes with higher expression specificity for the subiculum and neuron-rich areas of the cornu ammonis subfields and dentate gyrus. Within implicated regions, enrichments for schizophrenia and bipolar disorder were pronounced in ST spots containing glutamatergic neurons. Genes with higher expression specificity for the granule cell layer and subgranular zone of the dentate gyrus were more significantly enriched for association with bipolar disorder than for schizophrenia.

Conclusions: Our findings support a role for glutamatergic neurons in all major subregions of the hippocampus in mediating common-variant liability to schizophrenia and bipolar disorder and provide evidence for the greater relative importance of the dentate gyrus in bipolar disorder.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

LieberInstitute/VistoSeg

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: cd8ca310eaf6ca04909e6ca3a4e36d721c5b1d1d, 3 August 2023
Languages: MATLAB (12), R (7)
Size: 86 files, 19 scripts
Software Heritage: not archived
Found in: the text, “Cellular Deconvolution of ST Spots”
Holds: README, license file, continuous integration, 7 notebooks
Not found: CITATION.cff, environment file, tests, documentation
Tools: Image Processing Toolbox (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/LieberInstitute/VistoSeg

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 keywords, 2 funders, 39 references.

Cite

This paper

Baran, Y., Cameron, D., Pryce-Roberts, A., Richards, A. L., Webber, C., O’Donovan, M. C., & Bray, N. J. (2026). Spatial Mapping of Genetic Liability to Psychiatric Disorders in the Adult Human Hippocampus. Biological psychiatry global open science, 6(3), 100719. https://doi.org/10.1016/j.bpsgos.2026.100719

BibTeX

@article{baran2026spatial,
author = {Baran, Yusuf and Cameron, Darren and Pryce-Roberts, Adele and Richards, Alexander L and Webber, Caleb and O’Donovan, Michael C and Bray, Nicholas J},
title = {{Spatial Mapping of Genetic Liability to Psychiatric Disorders in the Adult Human Hippocampus}},
journal = {Biological psychiatry global open science},
year = {2026},
month = mar,
volume = {6},
number = {3},
pages = {100719},
publisher = {Elsevier},
issn = {2667-1743},
doi = {10.1016/j.bpsgos.2026.100719},
url = {https://doi.org/10.1016/j.bpsgos.2026.100719},
pmid = {42016326},
pmcid = {PMC13094429}
}

RIS

TY - JOUR
AU - Baran, Yusuf
AU - Cameron, Darren
AU - Pryce-Roberts, Adele
AU - Richards, Alexander L
AU - Webber, Caleb
AU - O’Donovan, Michael C
AU - Bray, Nicholas J
TI - Spatial Mapping of Genetic Liability to Psychiatric Disorders in the Adult Human Hippocampus
T2 - Biological psychiatry global open science
J2 - Biol Psychiatry Glob Open Sci
PY - 2026
DA - 2026/03/05
VL - 6
IS - 3
SP - 100719
SN - 2667-1743
PB - Elsevier
DO - 10.1016/j.bpsgos.2026.100719
UR - https://doi.org/10.1016/j.bpsgos.2026.100719
LA - en
ER -

CSL-JSON

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