On the Cutting Edge: The OMICS Generation

The last two decades have seen rapid development in almost every aspect of human life, and the field of biomedicine is no different. By the time the Human Genome Project concluded, the field of genomics had already observed a steady drop in the cost of sequencing. In years that followed, the technology developed rapidly, and the costs dropped to such a large extent that couldn’t even be explained by Moore’s law. Today, we live in the ‘OMICS generation’ where the use of sequencing has expanded beautifully to all the central dogma – Genomics (DNA), Transcriptomics (mRNA), Proteomics (Proteins), – and more, like Epigenomics (Epigenetics) & Metabolomics (Metabolites). All of this is happening at a very high resolution – at the single cell level – giving unprecedented insights into biology & medicine.

The use of methods that generate massive amounts of data has inevitably led to huge interdisciplinary collaborations amongst scientists. The data generated by a single OMICS experiment requires multiple quality control steps and bioinformatics analysis for it to even start making sense. Biologists, bioinformaticians, and computer scientists are now integrating life sciences, data science, and machine learning together to bring about and sustain the OMICS generation.

In this blog, we summarize some of the most popular and upcoming OMICS methods: scRNA-seq (single-cell RNA sequencing), ST (Spatial transcriptomics), scATAC-seq (single-cell Assay for Transposase Accessible Chromatin sequencing), and CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing). We also look at how computational concepts can be leveraged to study cellular behavior in vivo and lastly where the OMICS generation is leading us to.

Single-cell RNA sequencing

Today, single-cell RNA sequencing (scRNA-seq) is one of the most common methods being used in immunology and the rest of the biomedical fields. The tissue of interest is broken down to individual cells and then, using microfluidics or droplet-based methods, the mRNA of each cell from a batch of cells is barcoded, so as to keep track of transcripts of each cell. This is followed by reverse transcription and cDNA synthesis followed by sequencing. The number of cells used can vary from 50,000-1,000,000, and the number of reads obtained also vary depending on the sequencing depth.

In stark contrast to bulk RNA sequencing, the transcriptome of each cell of a sample can be studied separately. After the ‘wet-lab’ part is completed, the ‘dry-lab’ part takes over, where all the data needs to be cleaned up (quality control, normalization, batch effect corrections, etc.), and organized (dimensionality reduction & visualization)– before individual analyses can be performed. These include but are not limited to differential gene expression, gene set analysis (like gene ontology), and trajectory analyses1.

RNA velocity is an example of trajectory analysis where pre-mRNA and mRNA numbers are used to study cell dynamics (in contrast to the relatively static scRNA-seq analysis). For instance, if a cell has higher amount of pre-mRNA transcripts than its mRNA counterpart, it can be inferred that the cell was in the process of upregulating those pre-mRNA abundant genes and hence, changing its steady state transcriptome. This is especially useful in studies involving development and cells with changing phenotypes. Moreover, new packages for making the analysis more intuitive and insightful, while also attempting to streamline the process are being developed and published regularly.


The scRNA-seq lacks the spatial aspect owing to the disintegration of the tissue for the pooling of cells. To counter this, Spatialomics (ST) has emerged where the same barcoded primers mentioned above, are embedded on a glass surface. A tissue section is then placed over them, imaged and permeabilized, leading to mRNA from the tissue binding to the barcoded primers. The rest of the process is similar to scRNA-seq, with the difference that in the end, the single cell data can now be mapped spatially on to the image, giving an insight into the exact region of expression and tissue heterogeneity that otherwise would have been lost. Embedding the barcodes onto a slide is only one of the many ways being employed for ST and they are all summarized in a very recently published review by Moffitt and collegues2.

Assay for Transposable Accessible Chromatin sequencing and Multimodal analyses

At any given time, only a part of the genome is transcribable, while the rest is tightly coiled. The accessible chromatin landscape can provide essential insights into epigenetics of a given cell in a given tissue. Assay for Transposase Accessible Chromatin sequencing or ATAC-seq does this, by fragmenting the open regions and tagging them using Tn5 transposase which are then amplified by PCR and sequenced. ATACseq can also be performed on the single cell level by combining ATACseq technology to droplet-based methods3, making it possible to compare epigenetic signatures of individual cells from a given microenvironment.

Multimodal analyses have also emerged like CITE-seq and ASAP-seq4,5. CITE-seq refers to Cellular Indexing of Transcriptomes and Epitopes by sequencing where along with the usual scRNA-seq workflow, surface protein-specific antibodies conjugated with oligonucleotides are also included. Unlike fluorophores that are subject to fluorescence, these oligonucleotides are converted to cDNA and read along with the mRNA from the cell they are bound to. Hence, CITE-seq allows the use of virtually innumerable antibodies and ultimately helps in conducting a holistic surface-protein analysis (similar to flow cytometry) with the depth and complexity of the transcriptome. ASAP-seq (ATAC with Select Antigen Profiling by sequencing) works on a similar concept, but for chromatin accessibility. If you want to go one step further, read a merger of these two methods into (sc)CUT&TAG-pro, published a few months back by Zhang and collegues6.

Behavioral Profiling

Crainiciuc et al.7earlier this year showed how clustering algorithms and machine learning used in OMICS analysis can be expanded into something completely different: behavioral profiling. They imaged immune cells in inflammatory tissues in live mice (called intravital microscopy), and extracted relevant parameters relating to morphology (size, area, volume, shape, etc.) and kinetics (track duration, displacement, speed, etc.) to characterize cell types on basis of these behavioral parameters instead of mRNA or surface proteins. In one of the instances, using the appropriate number of such parameters helped them cluster macrophages, dendritic cells and neutrophils into precise populations, followed by sub-clustering to further discriminate on the basis of behavior. Using this approach, a great understanding of how cell populations really react to a given condition can be obtained, making behavioral profiling a unique and novel tool for intravital studies, especially in the context of immunology and inflammation where cell migration is central.

The necessary next steps

With researchers across the world generating OMICS data and many making it available for others to make use of, data integration is the next step, where single-cell data derived from different experiments can be integrated into one. This offers various challenges like experimental conditions and protocols used, sample preparation, sequencing depth, and so on. Integration tools are being developed to counter these problems and create massive atlases that could potentially simplify single cell analyses and also make them available to scientists who don’t necessarily make use of these technologies8. The Human Cell Atlas project, a worldwide effort to map every cell of the body using single-cell genomics and spatialomics also began in 2016. Knowing the state of cells without disease would enable future researchers to directly compare their diseased cells to the Atlas for reference.

With all these cutting-edge tools being developed that keep adding fuel to the scientists’ curiosity, it is safe to say that it’s a very interesting period of history to be existing in. And our generation might be fortunate enough to see more scientific breakthroughs in the coming decades.


1.     Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019 Jun 19;15(6):e8746. doi: 10.15252/msb.20188746

2.     Moffitt, J.R., Lundberg, E. & Heyn, H. The emerging landscape of spatial profiling technologies. Nat Rev Genet (2022). https://doi.org/10.1038/s41576-022-00515-3

3.     Lareau, C.A., Duarte, F.M., Chew, J.G. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol 37, 916–924 (2019). https://doi.org/10.1038/s41587-019-0147-6

4.     Stoeckius, M., Hafemeister, C., Stephenson, W. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14, 865–868 (2017). https://doi.org/10.1038/nmeth.4380

5.     Mimitou, E.P., Lareau, C.A., Chen, K.Y. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 39, 1246–1258 (2021). https://doi.org/10.1038/s41587-021-00927-2

6.     Zhang, B., Srivastava, A., Mimitou, E. et al. Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01250-0

7.     Crainiciuc, G., Palomino-Segura, M., Molina-Moreno, M. et al. Behavioural immune landscapes of inflammation. Nature 601, 415–421 (2022). https://doi.org/10.1038/s41586-021-04263-y

8.   Luecken, M.D., Büttner, M., Chaichoompu, K. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods 19, 41–50 (2022). https://doi.org/10.1038/s41592-021-01336-8

Article author: Kevin Merchant. Kevin is a MS student at LMU Munich, Germany, who is passionate about Immunology and writing. He aims to simplify latest research so that it becomes accessible to all.

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2 responses to “On the Cutting Edge: The OMICS Generation”

  1. Novel role of immune cells in hair growth – Antibuddies Avatar

    […] “conversation”, the scientists now used a computational software. They compared RNA-seq transcriptome data of wild-type mice and GR knockout mice and noticed a significant difference in the Tgfb3-Tgfbr […]


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