Methods for Single-Cell Multi-Omics

• Physical Separation: Methods like G&T-seq involve the physical separation of RNA and DNA from single cells, allowing for the separate sequencing of these molecules. This approach offers flexibility in downstream processing but can result in the loss of some molecules.
• Preamplification and Split: DR-seq co-amplifies RNA and DNA in a single reaction, which is then split for separate sequencing. This method minimizes analyte loss but can suffer from cross-contamination.
• Seq-Split: scONE-seq uses differentially barcoded primers for RNA and DNA, allowing for the co-amplification and sequencing of both molecules in a single reaction. This method is efficient but limits the ability to sequence libraries separately to optimal depths.
• Combinatorial Indexing: sci-L3-RNA/DNA co-assay uses a high-throughput approach to index and sequence RNA and DNA from large numbers of single cells. This method allows for high throughput but may have lower sensitivity.

Microfluidics in Single-Cell Multi-Omics

Microfluidics play a crucial role in single-cell multi-omics by enabling the precise manipulation and processing of individual cells. Microfluidic devices allow for the efficient isolation, lysis, and molecular barcoding of single cells, thereby improving the sensitivity and throughput of multi-omics analyses. Technologies like droplet-based microfluidics are particularly advantageous for high-throughput single-cell RNA sequencing, enabling the encapsulation of single cells in droplets for subsequent molecular barcoding and sequencing.

Methods for Spatial Multi-Omics

• Spatial Transcriptomics: Methods like MERFISH and Slide-seq allow for the spatially resolved analysis of transcriptomes within tissue sections. These methods use barcoded probes or bead-based arrays to capture spatial information.
• Spatial Proteomics: Techniques like CODEX and MIBI-TOF enable the spatially resolved analysis of proteins within tissues using multiplexed antibody staining and mass spectrometry.
• Spatial Epigenomics: Methods like Slide-DNA-seq allow for the spatially resolved analysis of epigenomic features within tissue sections.

Computational Integration

Integrating data from different molecular layers requires advanced computational tools to align and analyze multi-omics datasets. Techniques like machine learning and network analysis are employed to uncover the relationships between different molecular layers and to understand how they collectively influence cellular function.

Applications

• Cell Atlas Projects: Initiatives like the Human Cell Atlas aim to create comprehensive reference maps of all human cell types, leveraging single-cell and spatial multi-omics to understand cellular diversity and function.
• Disease Research: Multi-omics technologies are used to study the molecular mechanisms underlying diseases such as cancer, neurodegenerative disorders, and autoimmune diseases. These technologies can reveal the heterogeneity within tumors, the cellular composition of diseased tissues, and the molecular pathways driving disease progression.
• Developmental Biology: Single-cell and spatial multi-omics are used to study the molecular dynamics of development, from early embryogenesis to organogenesis. These studies provide insights into how cells differentiate and organize into complex tissues.

Challenges

• Technical Complexity: Multi-omics technologies require sophisticated instrumentation and expertise, posing a barrier to widespread adoption.
• Data Integration: Combining data from different molecular layers is computationally challenging and requires robust algorithms and computational infrastructure.
• Cost: The high cost of multi-omics experiments limits their accessibility, particularly for large-scale studies.

Future Directions

The future of single-cell and spatial multi-omics lies in:

• Improving Sensitivity and Resolution: Developing methods that can capture more molecular features with higher resolution and sensitivity.
• Scalability: Making technologies more scalable to enable large-scale studies across diverse biological systems.
• Integration with Clinical Practice: Translating multi-omics technologies into clinical diagnostics and personalized medicine, allowing for more precise disease diagnosis and treatment.

Conclusion

Single-cell and spatial multi-omics technologies have transformed our understanding of cellular biology by providing a comprehensive view of the molecular landscape within individual cells and tissues. Microfluidics has been instrumental in these advancements by enabling the precise manipulation and analysis of single cells. Continued advancements in these technologies and their integration into research and clinical practice promise to drive significant progress in our understanding of health and disease.

References

For more detailed information, please refer to the original article: Methods and applications for single-cell and spatial multi-omics, K. Vandereyken et. al published in Nature Reviews Genetics.