Table 3 Comparison of immunomics technologies at the single-cell level
Technology | Spatial | Strengths | Weaknesses |
|---|---|---|---|
H&E | √ | Simple intelligible protocol Lower cost and less time Impressive preservation of tissue morphology | Lack of specific markers Only morphological features and basophilic or eosinophilic information available |
mIHC&IF | √ | Highly specific marker Detailed information regarding the abundance, distribution and localization of certain substances | Spectral overlap Limited simultaneously detectable markers Time-consuming and labor intensive |
Flow cytometry | Â | Affordable and fast Machinery available in most institutes More tools available for analysis Could perform cell sorting | Spectral overlap Fluorescent spill-over Targets need to be selected carefully (biased) |
CyTOF | Â | More simultaneously detectable markers Higher accuracy without spectral overlap | Costly (both the machine and antibodies) Slower processing speed and lower sensitivity Targets need to be carefully selected (biased) |
Spectral flow cytometry | Â | Compatible with flow cytometry (both the machine and antibodies) Greatly eliminates confounding factors | Targets need to be carefully selected (biased) |
Single-cell seq | Â | Unbiased Parallel multi-omics analysis Generation of new hypotheses | Limited to nearly 10,000 cells Limited sequencing depth/coverage Costly, time-consuming and labor intensive |
CODEX | √ | Higher accuracy and specificity Detection of over 50 markers in a single slide | Affected by the tissue quality Accumulative structural changes Costly, time-consuming and labor intensive |
IMC | √ | At near-optical resolution Could be applied to biobanked tissues More simultaneously detectable markers | Lack of suitable commercial antibodies for use Comparatively lower rate of image acquisition Limited extent to which slides can be scanned Costly and only available in high-end facilities |
MIBI-TOF | √ | High accuracy at near-optical resolution Could be applied to biobanked tissue Indefinitely stable samples More simultaneously detectable markers | Lack of suitable commercial antibodies for use Comparatively lower rate of image acquisition Limited extent to which slides can be scanned Costly and only available in high-end facilities |
Spatial transcriptomics | √ | Visualization and quantitative analysis of the transcriptome with spatial resolution | Small-niche but not real single-cell sequencing Comparatively low resolution |
Slide-seq | √ | High spatial resolution High scalability to large tissue volumes Lower cost and better accessibility | Small-niche but not real single-cell sequencing Not suitable for analyzing multiple sections Confined to transcriptomics data |
HDST | √ | Higher spatial resolution than Slide-seq High scalability to large tissue volumes Lower cost and better accessibility | Small-niche but not real single-cell sequencing Not suitable for analyzing multiple sections Confined to transcriptomics data |
DBiT-seq | √ | Unbiased High spatial resolution multi-omics seq Compatible with different tissues High accessibility and operability | Small-niche but not real single-cell sequencing Existence of a theoretical limit of the pixel size |
ZipSeq | √ | Provides a complete map of live tissues May integrate with multimodal measurements | Confined to transcriptomics data Costly and only available in few facilities |
