Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Denis Bienroth; Natalie Charitakis; Dillon Wong; Yunhan C. Zhang; Sabrina Jaeger-Honz; Jialin Ding; Kevin I. Watt; Julian Stolper; Hazel Chambers-Smith; Duncan MacGregor; Bronwyn Christiansen; Celine Vivien; Adam T. Piers; Lisa N. Waylen; Lucas B. Hoffmann; Jessica Tang; Hue M. La; Mei R.M. Du; Monika Mohenska; Jose M. Polo; Sean Grimmond; Ethan Scott; Fernando J. Rossello; Enzo R. Porrello; Karsten Klein; Hieu T. Nim; David A. Elliott; Falk Schreiber; Mirana Ramialison

doi:10.1186/s13059-025-03630-6

Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Denis Bienroth, Natalie Charitakis, Dillon Wong, Yunhan C. Zhang, Sabrina Jaeger-Honz, Jialin Ding, Kevin I. Watt, Julian Stolper, Hazel Chambers-Smith, Duncan MacGregor, Bronwyn Christiansen, Celine Vivien, Adam T. Piers, Lisa N. Waylen, Lucas B. Hoffmann, Jessica Tang, Hue M. La, Mei R.M. Du, Monika Mohenska, Jose M. PoloSean Grimmond, Ethan Scott, Fernando J. Rossello, Enzo R. Porrello, Karsten Klein, Hieu T. Nim, David A. Elliott, Falk Schreiber, Mirana Ramialison

Research output: Contribution to journal › Article › Research › peer-review

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Abstract

The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics’ unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.

Original language	English
Article number	182
Number of pages	29
Journal	Genome Biology
Volume	26
Issue number	1
DOIs	https://doi.org/10.1186/s13059-025-03630-6
Publication status	Published - 2 Jul 2025

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10.1186/s13059-025-03630-6Licence: CC BY-NC-ND

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Bienroth, D., Charitakis, N., Wong, D., Zhang, Y. C., Jaeger-Honz, S., Ding, J., Watt, K. I., Stolper, J., Chambers-Smith, H., MacGregor, D., Christiansen, B., Vivien, C., Piers, A. T., Waylen, L. N., Hoffmann, L. B., Tang, J., La, H. M., Du, M. R. M., Mohenska, M., ... Ramialison, M. (2025). Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics. Genome Biology, 26(1), Article 182. https://doi.org/10.1186/s13059-025-03630-6

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title = "Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics",

abstract = "The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics{\textquoteright} unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.",

author = "Denis Bienroth and Natalie Charitakis and Dillon Wong and Zhang, \{Yunhan C.\} and Sabrina Jaeger-Honz and Jialin Ding and Watt, \{Kevin I.\} and Julian Stolper and Hazel Chambers-Smith and Duncan MacGregor and Bronwyn Christiansen and Celine Vivien and Piers, \{Adam T.\} and Waylen, \{Lisa N.\} and Hoffmann, \{Lucas B.\} and Jessica Tang and La, \{Hue M.\} and Du, \{Mei R.M.\} and Monika Mohenska and Polo, \{Jose M.\} and Sean Grimmond and Ethan Scott and Rossello, \{Fernando J.\} and Porrello, \{Enzo R.\} and Karsten Klein and Nim, \{Hieu T.\} and Elliott, \{David A.\} and Falk Schreiber and Mirana Ramialison",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",

year = "2025",

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doi = "10.1186/s13059-025-03630-6",

language = "English",

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Bienroth, D, Charitakis, N, Wong, D, Zhang, YC, Jaeger-Honz, S, Ding, J, Watt, KI, Stolper, J, Chambers-Smith, H, MacGregor, D, Christiansen, B, Vivien, C, Piers, AT, Waylen, LN, Hoffmann, LB, Tang, J, La, HM, Du, MRM, Mohenska, M, Polo, JM, Grimmond, S, Scott, E, Rossello, FJ, Porrello, ER, Klein, K, Nim, HT, Elliott, DA, Schreiber, F & Ramialison, M 2025, 'Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics', Genome Biology, vol. 26, no. 1, 182. https://doi.org/10.1186/s13059-025-03630-6

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AU - Bienroth, Denis

AU - Charitakis, Natalie

AU - Wong, Dillon

AU - Zhang, Yunhan C.

AU - Jaeger-Honz, Sabrina

AU - Ding, Jialin

AU - Watt, Kevin I.

AU - Stolper, Julian

AU - Chambers-Smith, Hazel

AU - MacGregor, Duncan

AU - Christiansen, Bronwyn

AU - Vivien, Celine

AU - Piers, Adam T.

AU - Waylen, Lisa N.

AU - Hoffmann, Lucas B.

AU - Tang, Jessica

AU - La, Hue M.

AU - Du, Mei R.M.

AU - Mohenska, Monika

AU - Polo, Jose M.

AU - Grimmond, Sean

AU - Scott, Ethan

AU - Rossello, Fernando J.

AU - Porrello, Enzo R.

AU - Klein, Karsten

AU - Nim, Hieu T.

AU - Elliott, David A.

AU - Schreiber, Falk

AU - Ramialison, Mirana

PY - 2025/7/2

Y1 - 2025/7/2

N2 - The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics’ unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.

AB - The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics’ unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.

UR - https://www.scopus.com/pages/publications/105009887392

U2 - 10.1186/s13059-025-03630-6

DO - 10.1186/s13059-025-03630-6

M3 - Article

C2 - 40598307

AN - SCOPUS:105009887392

SN - 1474-7596

VL - 26

JO - Genome Biology

JF - Genome Biology

IS - 1

M1 - 182

ER -

Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Abstract

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Defining immunotherapy response and resistance mechanisms in cutaneous squamous cell carcinoma.

Role of human non-coding DNA regulatory elements (REs) in heart development and disease

Monash Genomics & Bioinformatics Platform (MGBP)

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Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Abstract

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Projects

Defining immunotherapy response and resistance mechanisms in cutaneous squamous cell carcinoma.

Role of human non-coding DNA regulatory elements (REs) in heart development and disease

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Monash Genomics & Bioinformatics Platform (MGBP)

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