Open Research Europe

Part of EU Open Research Repository
Published July 19, 2022 | Version 2
Journal article Open

Qudi-HiM: an open-source acquisition software package for highly multiplexed sequential and combinatorial optical imaging

Authors/Creators

  • 1. Centre de Biologie Structurale, Centre National de la Recherche Scientifique, UMR5048, Montpellier, 34090, France
  • 2. Institut Agro Montpellier, INRAE, Montpellier, 34000, France

Description

Multiplexed sequential and combinatorial imaging enables the simultaneous detection of multiple biological molecules, e.g. proteins, DNA, or RNA, enabling single-cell spatial multi-omics measurements at sub-cellular resolution. Recently, we designed a multiplexed imaging approach (Hi-M) to study the spatial organization of chromatin in single cells. In order to enable Hi-M sequential imaging on custom microscope setups, we developed Qudi-HiM, a modular software package written in Python 3. Qudi-HiM contains modules to automate the robust acquisition of thousands of three-dimensional multicolor microscopy images, the handling of microfluidics devices, and the remote monitoring of ongoing acquisitions and real-time analysis. In addition, Qudi-HiM can be used as a stand-alone tool for other imaging modalities.

Files

openreseurope-2-16171.pdf

Files (3.6 MB)

Name Size Download all
md5:58adf654379549e62bba045169d239c3
3.6 MB Preview Download

Additional details

Cites
10.1038/s43586-021-00069-4 (DOI)
10.1038/nrm.2017.71 (DOI)
10.1126/science.aau1044 (DOI)
10.1038/s41556-018-0251-8 (DOI)
10.1038/s41596-019-0269-9 (DOI)
10.1126/science.aaa6090 (DOI)
10.1038/nmeth.2892 (DOI)
10.1126/science.aaf8084 (DOI)
10.1126/science.aau1783 (DOI)
10.1016/j.molcel.2019.01.011 (DOI)
10.1371/journal.pgen.1007872 (DOI)
10.1038/s41586-019-1035-4 (DOI)
10.1126/science.aar7042 (DOI)
10.1016/j.cell.2020.07.032 (DOI)
10.1038/s41586-020-03126-2 (DOI)
10.1038/s41576-018-0060-8 (DOI)
10.1101/cshperspect.a040378 (DOI)
10.1038/s41588-021-00816-z (DOI)
10.1016/j.tcb.2020.07.005 (DOI)
10.1038/s41586-020-2649-2 (DOI)
10.1038/s41592-019-0582-9 (DOI)
10.1007/978-3-030-00937-3 (DOI)
10.7717/peerj.453 (DOI)
10.3847/1538-3881/aabc4f (DOI)
10.1051/0004-6361/201322068 (DOI)
10.1016/j.softx.2017.02.001 (DOI)
10.1101/2021.01.18.427178 (DOI)
10.1063/1.4972392 (DOI)
10.1101/2021.03.12.435206 (DOI)
10.1371/journal.pbio.1001557 (DOI)
10.1038/s41592-021-01156-w (DOI)
Is new version of
10.12688/openreseurope.14641.1 (DOI)

References

  • Stelzer EHK, Strobl F, Chang BJ (2021). Light sheet fluorescence microscopy. Nat Rev Methods Primers. doi:10.1038/s43586-021-00069-4
  • Sahl SJ, Hell SW, Jakobs S (2017). Fluorescence nanoscopy in cell biology. Nat Rev Mol Cell Biol. doi:10.1038/nrm.2017.71
  • Sigal YM, Zhou R, Zhuang X (2018). Visualizing and discovering cellular structures with super-resolution microscopy. Science. doi:10.1126/science.aau1044
  • Schermelleh L, Ferrand A, Huser T (2019). Super-resolution microscopy demystified. Nat Cell Biol. doi:10.1038/s41556-018-0251-8
  • Cardozo Gizzi AM, Espinola SM, Gurgo J (2020). Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M. Nat Protoc. doi:10.1038/s41596-019-0269-9
  • Chen KH, Boettiger AN, Moffitt JR (2015). RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. doi:10.1126/science.aaa6090
  • Lubeck E, Coskun AF, Zhiyentayev T (2014). Single-cell RNA profiling by sequential hybridization. Nat Methods. doi:10.1038/nmeth.2892
  • Wang S, Su JH, Beliveau BJ (2016). Spatial organization of chromatin domains and compartments in single chromosomes. Science. doi:10.1126/science.aaf8084
  • Bintu B, Mateo LJ, Su JH (2018). Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science. doi:10.1126/science.aau1783
  • Cardozo Gizzi AM, Cattoni DI, Fiche JB (2019). Microscopy-Based Chromosome Conformation Capture Enables Simultaneous Visualization of Genome Organization and Transcription in Intact Organisms. Mol Cell. doi:10.1016/j.molcel.2019.01.011
  • Nir G, Farabella I, Pérez Estrada C (2018). Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling. PLoS Genet. doi:10.1371/journal.pgen.1007872
  • Mateo LJ, Murphy SE, Hafner A (2019). Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature. doi:10.1038/s41586-019-1035-4
  • Gut G, Herrmann MD, Pelkmans L (2018). Multiplexed protein maps link subcellular organization to cellular states. Science. doi:10.1126/science.aar7042
  • Su JH, Zheng P, Kinrot SS (2020). Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin. Cell. doi:10.1016/j.cell.2020.07.032
  • Takei Y, Yun J, Zheng S (2021). Integrated spatial genomics reveals global architecture of single nuclei. Nature. doi:10.1038/s41586-020-03126-2
  • Rowley MJ, Corces VG (2018). Organizational principles of 3D genome architecture. Nat Rev Genet. doi:10.1038/s41576-018-0060-8
  • Nollmann M, Bennabi I, Götz M (2022). The Impact of Space and Time on the Functional Output of the Genome. Cold Spring Harb Perspect Biol. doi:10.1101/cshperspect.a040378
  • Espinola SM, Götz M, Bellec M (2021). -regulatory chromatin loops arise before TADs and gene activation, and are independent of cell fate during early development. Nat Genet. doi:10.1038/s41588-021-00816-z
  • Wu YL, Tschanz A, Krupnik L (2020). Quantitative Data Analysis in Single-Molecule Localization Microscopy. Trends Cell Biol. doi:10.1016/j.tcb.2020.07.005
  • Harris CR, Millman KJ, van der Walt SJ (2020). Array programming with NumPy. Nature. doi:10.1038/s41586-020-2649-2
  • Berg S, Kutra D, Kroeger T (2019). ilastik: interactive machine learning for (bio)image analysis. Nat Methods. doi:10.1038/s41592-019-0582-9
  • Schmidt U, Weigert M, Broaddus C (2018). Medical Image Computing and Computer Assisted Intervention – MICCAI. doi:10.1007/978-3-030-00937-3
  • van der Walt S, Schönberger JL, Nunez-Iglesias J (2014). scikit-image: image processing in Python. PeerJ. doi:10.7717/peerj.453
  • Price-Whelan AM, Sipőcz BM (2018). The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. Astron J. doi:10.3847/1538-3881/aabc4f
  • Robitaille TP, Tollerud EJ (2013). Astropy: A community Python package for astronomy. Astron Astrophys. doi:10.1051/0004-6361/201322068
  • Binder JM, Stark A, Tomek N (2017). Qudi: A modular python suite for experiment control and data processing. SoftwareX. doi:10.1016/j.softx.2017.02.001
  • Phillips MA, Pinto DMS, Hall N (2021). Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science. bioRxiv. doi:10.1101/2021.01.18.427178
  • Barabas FM, Masullo LA, Stefani FD (2016). Note: Tormenta: An open source Python-powered control software for camera based optical microscopy. Rev Sci Instrum. doi:10.1063/1.4972392
  • Fox ZR, Fletcher S, Fraisse A (2021). MicroMator: Open and Flexible Software for Reactive Microscopy. bioRxiv. doi:10.1101/2021.03.12.435206
  • Barho F, Fiche JB, Nollmann M (2022). qudi-HiM (0.1.0). Zenodo.
  • Fiche JB, Cattoni DI, Diekmann N (2013). Recruitment, assembly, and molecular architecture of the SpoIIIE DNA pump revealed by superresolution microscopy. PLoS Biol. doi:10.1371/journal.pbio.1001557
  • Montero Llopis P, Senft RA, Ross-Elliott TJ (2021). Best practices and tools for reporting reproducible fluorescence microscopy methods. Nat Methods. doi:10.1038/s41592-021-01156-w
  • Nollmann M, Espinola S (2022). Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines (Version 1) [Data set]. Zenodo.