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Home » Context is key: unlocking tissue complexity through space biology
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Context is key: unlocking tissue complexity through space biology

March 13, 2023No Comments5 Mins Read
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Keep up to date with the latest scientific advances with Brush Up Summaries.

What is Space Biology?

Spatial biology is a series of techniques with which scientists collect detailed cellular information and examine the positional context of cells in a tissue. Individual cells use their genes, RNA and proteins differently in different tissue types. Spatially resolved analyzes provide insight into new strategies to prevent or treat disease, including infections, cancer, neurological disorders, and metabolic disorders.1.2

What can space biology researchers learn?

Spatial transcriptomics
Transcriptomics encompasses studies in which researchers examine gene expression dynamics and heterogeneity with RNA sequencing. In spatial transcriptomics, also called spatial genomics, methods of capturing positional transcriptome information often unite the previously separate fields of imaging and sequencing.2 Scientists rely on a variety of microscopy-based methods and complementary transcription probes to record the location of different RNA species in tissues, either directly or prior to targeted sequencing. Some examples of these methods include in situ hybridization (ISH), in situ sequencing (ISS), spatially encoded probe arrays coupled with next generation sequencing (NGS), and microdissection.1

Spatial proteomics
Spatial transcriptomics is not the only -omics approach to study the immense complexity of biological systems. Just as in situ hybridization-based imaging and cutting-edge sequencing have brought spatially-resolved transcriptomes to the forefront of research, scientists are using established and new methods to spatially analyze protein distribution at tissue levels, cellular and subcellular. Spatial proteomics methods include immunohistochemistry (IHC), immunofluorescence (IF), mass spectrometry (MS), and cytometry. These techniques have varying depth of coverage and throughput.3

Visual representation of four spatial transcriptomics techniques (top to bottom, in situ hybridization (ISH); in situ sequencing (ISS); barcode arrays paired with NGS; and microdissection).

In spatial transcriptomics, RNA species are directly visualized in intact tissues with labeled probes, or researchers record the locations of transcripts before extraction for sequencing. In ISH, scientists repeatedly image the same probes with different fluorophores to create a gene-specific barcode for a region of interest (ROI). Similarly, imaging multiple short probes hybridized along an amplified transcript for the ISS allows researchers to visually determine the target sequence. Common NGS-based methods include ligation of RNAs to spatial barcoded probes by overlaying tissue on a microarray, or microdissection of hybridized probes with targeted UV light on ROI prior to NGS.1

Advantages and limitations of current space biology methods
Before the advent of spatial transcriptomics, single-cell RNA sequencing methods largely required scientists to separate tissues into individual cells (eg, islet cells of pancreatic tissue). As a result, earlier technologies have lost the spatial context needed to fully understand biological processes such as cell-to-cell interactions between normal and diseased tissues, and how unique cell types contribute to heterogeneous tissues.

Spatial transcriptomics methods address this challenge by capturing entire areas of tissue. Using these methods, scientists infer the single-cell resolution of the transcriptome with the spatial context.3 However, spatial analysis of transcriptome-wide information from all individual cells in a tissue sample is not yet a routine process.2 Some additional challenges for spatial genomics and spatial proteomics analyzes include tissue suitability for specific methods (e.g., autofluorescence of human brain tissue, which can complicate fluorescence imaging-based techniques) and limitations detection for rare cells and low copy number transcripts. Additionally, many imaging and sorting methods rely on low-throughput antibody-based detection, which can pose a challenge for molecular targets without established antibodies.1.3

Spatial Biology in Practice: Cell Atlas Mapping Across Tissue Types

Space biology is a growing field, thanks in part to the improved accessibility of NGS, as well as initiatives such as the Human Cell Atlas (HCA).1 The HCA project is an international collaboration of scientists aiming to define all human cell types in terms of distinctive molecular profiles and cellular descriptions such as location and morphology.4 Researchers in the HCA community have already contributed to new fundamental biological discoveries with potential for clinical applications.5

For example, HCA researchers have reported single-cell data that highlight cellular heterogeneity in a multitude of tissues, including the heart, liver, intestine, pancreas, thymus, and brain. Spatial examination of transcriptomes from different tissues leads to new insights, such as a better understanding of sex-linked risk factors in heart disease. It also reveals complexity that was previously overlooked, such as identifying epithelial progenitor cells in the liver or pancreatic cell type-specific neighborhoods with unexpected cell-to-cell interactions.5

Beyond new insights into specific tissue types, the HCA project aims to generate a comprehensive reference list of all cell identities and characteristics throughout the body. Such a list will accelerate fundamental understanding and translational science. The power of spatial biology approaches in HCA research will inform scientists about which cells express different genes of interest, which cell types are present in each tissue, and which cell types coexist in spatial proximity to each other.5

The references

  1. CG Williams et al., “An introduction to spatial transcriptomics for biomedical research,” Genome Med14(1):68, 2022.
  2. V. Marx, “Method of the year: spatially resolved transcriptomics”, National Methods18(1):9-14, 2021.
  3. A. Mund et al., “Spatially Unbiased Proteomics with Single Cell Resolution in Tissues,” Mol Cell82:2335-49, 2022.
  4. A. Regev et al., “The Atlas of Human Cells”, eLife6:e27041, 2017.
  5. RGH Lindeboom et al., “Towards an atlas of human cells: taking notes from the past”, Genet Trends37(7):625-30, 2021.
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