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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5346_Библиотеки_им_академика_М_И_Перельмана
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the robust tools for data analysis and visualization available for CyTOF mass cytometry
[129]. Thereby, IMC and MIBI have been successfully applied to deep profiling of the
TIME in FFPE tissue sections [130–132]. While both systems require considerable
up-front investment to obtain the scanner, metal-tagged antibodies are now being
offered by multiple suppliers individually and in multimarker panels. While scanning
speed, spatial resolution, and sensitivity are not competitive with IF or FISH, MSIHC
has advantages over fluorescence methods with similar depth of profiling in that all
the channels are collected at once. On balance, MSIHC can provide unique capabilities
that offset current limitations.
3.6 Interrogating the TIME by immunodetection and ISH in 3-D
While the gold standard method for pathological and clinical diagnostics remains analysis
of single thin tissue sections, the marked heterogeneity of tumors and the TIME cannot
be sampled adequately in one plane, arguing for extending the analysis to three dimensions (3-D). A single thin section may lack morphological features important to evaluating a biopsy that might be appreciated in other parts of the block, such as pushing
margin, lymphocytic infiltrate, scar formation, and squamous change. In particular, it
is not possible to appreciate vascular networks in thin sections, frustrating analysis of
microscopic pharmacokinetics and pharmacodynamics. Robust tools to interrogate
the TIME in 3-D might have major impacts on the use of current and emerging antibody
and cellular therapies.
Constructing 3-D maps based on chromogenic IHC or fluorescent IHC/IF stained
sections is feasible, albeit slow and awkward, based on staining serial sections in parallel,
aligning images, and 3-D reconstruction [133,134]. A critical factor in reconstructing
3-D tissue volume from serial sections is consistent registration from section to section
[135,136]. Automated 3-D reconstruction tools [137–141] can tolerate typical sample
and staining defects but may be sensitive to tears, folds, and other common microtomy
artifacts. Overall, avoiding physical slicing in favor of optical sectioning would appear
preferable.
309Spatial mapping of the tumor immune microenvironment
3.6.1 Tissue clearing-based 3-D multiplex fluorescence microscopy
Direct 3-D microscopy for obtaining high-resolution volumetric images from tumor tissue has become straightforward based on recent advances in the optical clearing of tissue.
Light absorption and scattering are major barriers to 3-D imaging of fresh or embedded
tissue using visible light as with a conventional fluorescence microscope [142,143].
Although many tissues are semitransparent in the near-infrared (NIR) [62,144–146], this
is not a satisfactory detection band for high resolution fluorescence microscopy.
A practical solution, first demonstrated over a hundred years ago, is to match or equalize
the refractive index within a tissue [145,147,148]. With the recent development of a
wide range of organic, aqueous, and gel-based methods for optical tissue clearing that
310 Yi-Chien Wu et al.
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preserve morphology and macromolecular integrity and do not interfere with probe
binding or quench fluorescence, the increased imaging depth has enabled multiplexed
IF, FISH, and other approaches in 3-D [144–146,148,149].
Among the many options for tissue clearing [146,150 –156], not all are well-suited to
3-D imaging of the TIME. Many of these technologies are based on CLARITY
[144,145], where tissue is initially soaked in formaldehyde, acrylamide monomer, and
thermal initiator at 4°C. Formaldehyde crosslinks acrylamide to proteins and nucleic
acids, yielding a tissue hydrogel upon heating to 37°C to initiate polymerization. Membrane lipids are then removed with SDS. The porous hydrogel is readily penetrated by
fluorescent probes, enabling multiplex IF and FISH. Cyclic staining is also feasible to
increase the depth of profiling. The whole process can take days to weeks, limiting
throughput. Another drawback is that plasma membrane-localized antigens, such as
the cluster of differentiation (CD) markers critical for evaluation of the TIME, can be
poorly preserved. Alternative approaches are well-described that avoid the slower and
most destructive steps in the CLARITY process without losing the ability to examine
intact tissues in 3D at cellular resolution.
One of these practical alternatives that is particularly well-matched to the analysis of
the TIME is 3-D multiplex imaging of tumor tissues by transparent tissue tomography
(T3) [157–159] (Fig. 7). T3 is a nondestructive approach that applies light fixation
and aqueous tissue clearing with concentrated solutions of D-fructose as used for SeeDB
[153] to speed up tissue processing, clearing, and imaging [157–159]. For T3 analysis,
tumor tissue is briefly fixed in formaldehyde, embedded in agarose, and sectioned with
a vibratome. The macrosections are then stained with cocktails of fluorescently labeled
antibodies, as used in flow cytometry. After clearing, the samples are analyzed by optical
sectioning with confocal microscopy and reconstructed to yield a 3-D tumor image
[157–159]. T3 has been applied successfully for studying mouse tumor responses to
immunotherapy and to characterize the TIME of human tumors. T3 is able to track
the distribution of therapeutic antibody [157] or nanoparticles [160] in the context of
the microvasculature [161] and with monitoring of anticancer immune response.
4. Spatial ’omics
Methods based on interrogating the TIME with labeled probes are intrinsically
limited by bias in the selection of the targets. However, recent adva nces in spatial
that allow detection of hundreds to thousands of proteins and/or transcripts at a time
while mapping their distributions at near-cel lular resolution are transforming tissue
analysis.
0
4.1 Spatial transcriptomics
Although long imagined, it is only recently that advances in methods and informatics for
transcriptomics have become sufficiently robust to enable single-cell RNA sequencing
omics
311Spatial mapping of the tumor immune microenvironment
Fig. 7 Workflow of Transparent Tissue Tomography (T3). Excised tumors undergo 10-min fixation with
2% paraformaldehyde. Next, the tissue is embedded in 2% agarose. Embedded tissue is cut into 400-μ
m thick on a microtome. Each section is stained overnight with a cocktail of fluorescent primary antibodies. The next day, sections are optically cleared in 80%–100%
confocal microscope. Tile scan results in Z-stack images, that are further registered and reconstructed
for visualization and analysis of 3-D tumor images.
D-fructose solutions and imaged by a
(scRNA-seq) [162], which has since been commercialized [163], making a dramatic
impact on the analysis of the TIME and response to immunotherapy [164]. Subsequent
developments are now enabling spatial transcriptomics (ST) at cellular resolution in tissue
[165–167], which may yield similar impacts in cancer immunotherapy. Here, we discuss
several pioneering approaches including laser capture microdissection (LCM)-mediated
ST, DNA microarray chip-based in situ ST, and microfluidics-based ST (Table 1). No
methods are yet suited to fully map transcription across a tissue section at cellular resolution, but this goal now seems within reach. Here, we also discuss the advantages, shortcomings, and future prospects of the ST approaches.
Table 1 Overview and comparison of spatially-resolved ’omics technologies.
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Spatial
Method Sample type Target Approach
resolution
Advantages (+)
and drawbacks
(2)
TIME
studies
Coupled seq
approach
Tissues
studied Reference
LCM-seq Cryosection
mRNAs Area-driven
and FFPE
PHLI-seq Cryosection mRNAs
and
proteins
ST Cryosection mRNAs
and
proteins
Slide-seq,
Cryosection mRNAs TranscriptomeSlideseqV2
transcriptomewide
Area-driven
transcriptomewide
Transcriptome-
wide and
targeted antigens
Commercialized as
10X Genomics
Visium
wide
Cellular + Robust
+ Selective
Low
throughput
Cellular + Less
damaging
than
LCM-seq
Low
throughput
50-μm pixel
microarray
+ Multiomics
analysis
+ High
sensitivity
Biased
protein
detection
Very low
spatial
resolution
10-μm
microbeads
+ Near-cellular
resolution
Low
sensitivity
Yes Smart-seq2 Multiple [168]
No Whole-
exome
and
Human
breast
cancer
[169]
targeted
Yes paired-end
Multiple [170]
NGS
No SOLiD Multiple [171]
HDST Cryosection mRNAs Transcriptome-
wide
DBiT-
seq
Cryosection
and FFPE
mRNAs
and
proteins
Transcriptome-
wide and
targeted antigens
2-μm
microbeads
in wells
+ Sub-cellular
resolution
Low
sensitivity
10-μm pixels + Near cellular
resolution
+ Multiomics
analysis
+ High
sensitivity
Biased
protein
detection
Low spatial
resolution
Yes MARS-seq Human
breast
cancer
No Paired-end
NGS
Mouse
embryo
[172]
[173]
314 Yi-Chien Wu et al.
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4.1.1 LCM-mediated spatial transcriptomics
In LCM, cells are selected based on morphology or antibody staining and then excised
from thin sections for DNA, protein, or RNA analysis [174]. Applied to analysis of the
TIME, LCM can enable gene expression within individual immune cells. This approach
is especially important for rare but distinct immune cell populations whose molecular
information would be masked normally when analyzing the bulk tumor.
Since the first practical demonstration of LCM in 1996 [175], multiple commercial
systems have become available, and the method is commonly used in clinical pathology
labs to examine patient samples. LCM systems typically use ultraviolet (UV) or infrared
(IR) light aimed via a microscope objective to select cells and clusters for capture
[176,177] (Fig. 8). In UV LCM, cells of interest are cut away from surrounding tissue
and captured. In IR LCM, cells of interest adhere to thermoplastic film after heating by
alow-powerlaser.
Both IR and UV formats for LCM offer precise cell selection and dissection and preservation of spatial information (Fig. 8). Taking advantage of IF or other methods to identify immune cells in the tumor stroma, LCM has been proven as a robust tool for
expression profiling of the TIME [178–180]. LCM has been applied to examine the
transcriptomes of single neurons captured from frozen sections [168,181], suggesting
the potential to extend single-cell LCM to the TIME. A concern is that RNA isolated
from FFPE tissue sections is typically of low quality due to tissue processing and then
maybe further degraded during cellular dissection for LCM, suggesting that IR LCM systems may be preferable to UV for transcriptome analysis [176]. Drawbacks are that the
LCM procedure is destructive, low throughput, and can only sample a small fraction of a
tissue section [182–184]. Future directions for LCM-seq might include increased precision, higher throughput, and fully automated microdissection, which could meet the
need for whole tissue analysis. An example of emerging capabilities is provided by
PHLI-seq [169], a next-generation LCM technology developed to examine genetic heterogeneity in tumors. In PHLI-seq, tissue sections are processed for H&E staining on
indium tin oxide (ITO)-coated glass slides. Clusters of cells are selected and isolated with
a single shot of a 1064 nm infrared laser, which vaporizes the ITO to release the cells for
genomic analysis. Single-cell resolution LCM analysis has recently been demonstrated
[185]. This technology appears capable of being adapted to transcriptome analysis.
4.1.2 Microarray barcoding spatial transcriptomics
Building on prior studies of in situ RNA sequencing in tissue sections [186,187] and
methods for distinguishing cellular transcriptomes used in scRNA-seq, Stahl et al.
[170,188] described a creative strategy for spatial transcriptomics based on tiling the sur-
face of a glass slide with a microarray of positionally-barcoded oligo(dT) primers. A tissue
315Spatial mapping of the tumor immune microenvironment
Fig. 8 The design of IR LCM and UV LCM. Left, IR-LCM workflow in which thermoplastic film is used with
an IR laser to transfer target cells onto film for LCM analysis at infrared wavelengths. Right, UV-LCM
workflow using gravity to collect cells of interest excised from the sample by a UV laser into lysis buffer
for subsequent analysis by LCM at UV wavelengths. For UV-LCM, cells of interest can be collected with
gravity (center panel) or against gravity (lower right panel). (Modified from Nichterwitz S, et al. LCM-Seq: a
method for spatial transcriptomic profiling using laser capture microdissection coupled with PolyA-based
RNA sequencing. Methods Mol Biol 2018;1649:95–110. Zhu S, et al. Advances in single-cell RNA sequencing
and its applications in cancer research. Oncotarget 2017;8(32):53763–53779.)
316 Yi-Chien Wu et al.
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section is laid over the microarray, and cells are permeabilized to allow poly-A mRNA to
bind the closest oligo(dT) capture primers. Then, after cDNA synthesis, the protein and
RNA are removed by enzyme digestion, leaving only the tethered cDNA on the microarray. The cDNA then yields a library where each molecule carries a position barcode,
allowing deconvolution of the data after sequencing. Finally, the mRNA expression at
each position can be used to construct a map of gene expression in the tissue section.
Microarray ST has been commercialized by 10X Genomics as Visium, using 55-μm
diameter spots at 100-μm spacing with oligos bearing 16 nt spatial barcodes, 12 nt unique
molecular identifiers (UMIs), and 30 nt poly(dT) for poly-A mRNA capture. As with
Chromium scRNA-seq, Visium is also compatible with the codetection of proteins using
antibody-oligonucleotide conjugates, analogously to CITE-seq [115–117], and sensitive
RNA detection from FFPE tissue. Visium HD will soon offer 10 μm resolution, albeit
with reduced sensitivity. Targeted detection of transcripts may help offset this limitation.
A limitation of microarray ST is that each spot captures mRNA from a relatively large
number of cells. A practical answer is to combine microarray ST with scRNA-seq to
obtain deeper transcriptional profiling and deconvolute each ST spot to detect the contributing cell types [189,190]. In multimodal intersection analysis (MIA) (Fig. 9) [189],
part of the tumor sample is processed by tissue dissociation for scRNA-seq and another
part by cryosectioning for ST analysis. Thereby, scRNA-seq yields a cell type-specific
gene set, and microarray ST provides a region-specific gene set, providing a map of cell
types in the tumor. Applied to pancreatic cancer, Moncada et al. [189] were thereby able
to map multiple subpopulations of innate and adaptive immune cells in the TIME. Combining 10X Chromium with targeted Visium HD may provide a practical route to single
cell resolution MIA in FFPE sections, making “virtual ST” poised for application in clinical studies of immunotherapy response.
4.1.3 Microbead barcoding spatial transcriptomics
Arrays of micrometer-sized, barcoded particles (microbeads) randomly scattered on a
surface and then spatially decoded were i ntroduced as a practical alternative to printed
or synthesized-in-place flat microarrays nearly 2 0 years ago [191]. Along these lines, in
Slide-seq [171], 10- μm microbeads bearing oligo(dT) capture primers that also encode
a microbead barcode and a unique molecular identifier (UMI) code as used for dropletbased scRNA-seq [192] are packed into a monolayer on a slide and the positions of each
microbead are decoded using sequencing-by-ligation in situ. mRNA released from cells
in cryosections placed on the microbead array is reverse transcribed in situ, and then the
microbeads are collected and processed for library preparation as in scRNA-seq. The
microbead data can be processed to map each mRNA back to the t issue, creating a multidimensional map. Although Slide-s eq offers higher spatial resolution than microarray
Fig. 9 Multimodal intersection analysis using microarray ST and scRNA-seq. Surgically dissected tumors are divided for (upper) scRNA-seq and
(lower) ST analysis. Uniquely or differentially expressed genes found by scRNA-seq analysis are used for the determination of cell types and celltype-specific gene sets within the tumor sample. The remaining tissue is cryosectioned and mounted onto spatially barcoded ST microarray for
gene expression-guided tissue architecture mapping. Then, multimodal intersection analysis is applied to integrate the two datasets and construct a spatial map of the distribution of cell populations. (Reproduced with permission from Moncada R, et al. Integrating microarray-based spatial
transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol 2020;38(3):333–342.)
318 Yi-Chien Wu et al.
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ST, the 10-μm microbeads will sample multiple cells and with a recovery of transcripts
of only a bout 50 per microbead, the method is not well-matched to characterizing the
TIME. A near 10-fold higher yield of transcripts based on a new scheme for position
decoding, improved microbead synthesis, and optimized library synthesis can be
achieved with Slide-seqV2 [193], which may be sufficient to recognize major cell types
identified by e.g. scRNA-seq.
A second microbead-based strategy, high-definition spatial transcriptomics (HDST)
[172], offers higher spatial resolution by adapting the Illumina BeadArray technology to
spatial transcriptomics. For HDST, barcoded 2-μm silica microbeads are loaded into
microwells etched in a fiber bundle to form a hexagonal array [194,195], and microbead
positions are decoded by hybridization analysis with fluorescent probes [196]. To facilitate
mRNA capture, cDNA synthesis, and sequencing, the microbeads are coated with 5
0
tethered primers that include a cleavage sequence, sequencing adapter, the spatial barcode, a
UMI, and an oligo(dT) tail. After permeabilizing fixed and H&E stained cryosections,
polyA-mRNA is captured onto the microbead array. After reverse transcription in situ,
the barcoded primers and linked cDNA are cleaved off, processed into sequencing libraries
and the resulting transcriptome mapped via the barcodes to positions in the tissue section.
The high spatial resolution of HDST and the correlation with morphology using H&E
allows microbeads to be assigned to a single cell type or even subcellular features.
A significant concern is that of the many thousands of mRNAs that might have been
detected, less than ten transcripts could be assigned to each microbead, suggesting the need
for increased efficiency at multiple steps. However, anticipating further development and
potential commercialization, HDST appears poised to serve as a useful tool for spatial transcriptomic and multiomic analysis with single-cell resolution in the TIME.
4.1.4 Microfluidic barcoding spatial transcriptomics
Deterministic barcoding in tis sue for spatial omics sequencing (DBiT-seq) [173] may be
the first example of a new class of assays for spatial tra nscriptomics and multiomics in
tissue sections (Fig. 10). Here, barcoded oligo(dT) primers are introduced to the FFPE
tissue section via microchannels. After in situ cDNA synthesis, the second set of barcodes
are applied by a set of perpendicular microchannels and are ligated to the first barcode,
generating a 2-D mosaic of combinatorial barcodes in the tissue at 10-μm resolution.
Antibody-oligonucleotide conjugates bearing barcodes and oligo(dA) tails can be
applied and detected in parallel as in CITE-seq [115–117]. After digesting the tissue,
the spatially barcoded cD NA and antibody tags are processed for sequencing.
Deconvoluting the barcodes enables spatial comapping of genomic and proteomic
profiles in a tissue section at roughly cellular resolution. Although DBiT-seq and
Slide-seq/Slide-seqV2 have equivalent pixel resolution, DBiT-seq appears to recover
more transc ripts per 100 μm
2
pixels. Were DBiT-seq taken to practical limits, poten-
tially reaching the spatial resolution of HDST, and then grouping pixels from
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