Breaking Down the Cell’s Secrets

You are currently viewing Breaking Down the Cell’s Secrets

As 2020 was all set to mark the beginning of the third decade of this century, the year also proved to be the year for biology. With the COVID-19 pandemic still going strong in several countries, societies and governments have turned to biology researchers in academia and industries alike for potential vaccines and cures against the infamous SARS-Cov-2 virus. These aims cannot be met without novel solutions and innovations in biological research from recent years that are proving to be more significant by the day. 

In this article, we review certain key innovations in biology that have managed to aid advances in basic research as well as in therapeutics.One such technique is Cryo-electron microscopy (Cryo-EM) that helped elucidate crucial aspects of initial structures of the SARS-Cov-2 viral spike proteins and its binding partners. 

Cryo-EM involves flash-freezing or plunge-freezing of proteins and other biomolecules that are then subjected to high energy electron beams. Electron bombardment on these frozen biomolecules is detected and used to reconstruct their 3D shapes and structures. Unlike the conventionally used X-ray crystallography that studies protein structures using their characteristic fingerprints formed by the diffraction of X-rays, Cryo-EM does not require the tedious process of crystallization. It can thus be applied to a wider variety of proteins and their complexes, previously thought far from amenable for structural studies, such as those found in ribosomes. Cryo-EM has also proven to be important and better than X-ray crystallography for a particular category of proteins called membrane proteins that are embedded in cellular or organellar membranes. 

Recently, the state-of-the-art for Cryo-EM structure resolution has reached <1.5 Aº for apoferritin (Yip et al 2020) and homo-pentameric β3 γ-aminobutyric acid type-A receptor (GABAAR) (Nakane et al 2020). These feats were achieved primarily due to advancements in hardware and data analysis techniques. Another significant contribution of the Cryo-EM revolution has been electron Cryotomography (CryoET) (Villa et al 2013) where thin slices of cryopreserved samples are imaged by tilting them at an inclination of 70º, and simultaneously subjecting them to an electron beam. Along with creating a three-dimensional cellular reconstruction, this technique also resolves molecular structures for more abundant and recurrent proteins to less than a nanometer (Briggs 2013). Reaching sub-nanometer resolution also requires computational tools like single particle tracking algorithms that have aided in enhancing and refining the data analysis. Although we can achieve such resolutions, identifying a  molecule at its appropriate position within a cell has remained a key challenge. To circumvent this obstacle, Cryo-EM is now being coupled with correlative light microscopy. Here, proteins of interest are labelled with fluorescent probes, imaged using conventional fluorescence- or super-resolution microscopy techniques to locate the region of interest and then Cryo-EM is performed on this particular region. Correlative microscopy provides the requisite contrast essential to optimize cellular explorations at the sub-nanometer scale. However, the information gained from Cryo-EM is from samples that are fixed and provide snapshots of the highly dynamic nature of molecules within a cell. How do we gain insights into the dynamic nature of the molecular compositions within a cell? And how can we quantify a cell’s response to its changing environment and simultaneously track the changes in a spatio-temporal manner?

 These questions can be answered through the umbrella term of ‘Single-Cell Technologies’, where one can study the protein, RNA and DNA make-up of a single cell and study the effects of various parameters like drugs, injury, and cellular environment on cellular behaviour. Since the first set of single-cell transcriptome (net RNA content of a cell) data, breakthroughs in this field have largely been driven through innovations in microfluidics, molecular biology, computational tools and imaging technologies. These approaches have not only given way to understanding the mRNA content, context of a cell and the transcriptome, but have also enabled explorations into parameters like protein quantification, chromatin organization and histone distribution and modifications, that are essential to understanding single-cell functions. 

The current state-of-the-art apparatus involves breaking down a tissue into its cellular components and then using these cells for downstream analyses. The emerging insights include an understanding regarding how cells gain and preserve their positional information (in three-dimensions) in a developing embryo. We can now begin to establish connections between ‘phenotype’ and ‘genotype’ in a bottom-up manner, starting from a single cell, combining quantitative information regarding its molecular make-up, its position within a tissue and the context within which it eventually performs its functions. 

Exploration regarding positional information at the single-cell level has been possible by employing extant models such as mice, zebrafish and organoids (laboratory-grown organs). In these systems, changes in cell fates of embryonic cell populations are traced using tools like barcoded reporter oligonucleotides or CRISPR-Cas mediated sequential genetic changes and ultimately help construct lineage-trees (Spanjaard et al 2015; Chan et al 2019; Raj et al 2018). 

But an interesting discovery that has surprised scientists is that many cell populations may not be as permanent and homogeneous as previously thought (Trapnell et al 2015). Several nodes on these lineage-trees may appear transiently and differentiate into subsequent populations or contrarily may arise in a context- dependent manner to perform certain specific functions. Single-cell sequencing and related computational approaches have therefore provided a window to explore how cellular microenvironments can have varied effects on molecular compositions and hence, their respective functions. Fascinatingly, these techniques have also enabled discoveries of previously unknown cell types like the lung ‘ionocytes’ that mediate the lung pathology characteristic to cystic fibrosis by overexpressing cystic fibrosis transmembrane conductance regulator (CFTR) (Plasschaert et al 2018). Another example is the discovery of several novel populations of dendritic cells (AS DCs), two new monocyte populations and a few other transient cells (Villani et al 2017). 

The ways in which single-cell techniques find their way from bench to bedside have been aided by the possibility of culturing 3D organoids in the laboratory. These organoids are derived from patient stem cells and can be easily subjected to genetic manipulation, drug screens, and developmental studies for various organs like stomach, intestine, brain, kidney, liver, etc. They have also enabled understanding disease phenotypes and drug effects on developmental trajectories that are very corroborative. For example, cerebral organoids have provided insights into compromised neuronal migration in patients with periventricular nodular heterotopia (Klaus et al 2019). 

However, the current approaches are limited by the throughput of the number of cells due to time, data and technical constraints.Further, the organoid based single-cell trajectories may not recapitulate the exact functioning of a complex organ such as the brain although the morphology may be reconstructed. Therefore, we need to understand the underlying principles that not only give rise to the form but also to the function of an organ by further refining the multi-parametric state-space that single-cell technologies have added to our arsenal. 

In summary, it is emergent that these innovations are poised to open new avenues for research and discovery. Be it the structure of single molecules in their cellular position or their relative abundance under various environmental landscapes, these surely will contribute novel insights and cues to the long-standing puzzle, ‘What is Life?’

References

  1. B. Spanjaard et al., Nat. Biotechnol. 36, 469 (2018). 10.1038/nbt.4124pmid:29644996
  2. C. Trapnell, Genome Res. 25, 1491 (2015). 10.1101/gr.190595.115pmid:26430159
  3. M. M. Chan et al., Nature 570, 77 (2019). 10.1038/s41586-019-1184-5pmid:31086336
  4. B. Raj et al., Nat. Biotechnol. 36, 442 (2018). 10.1038/nbt.4103pmid:29608178
  5. A. C. Villani et al., Science 356, eaah4573 (2017). 10.1126/science.aah4573pmid:28428369
  6. L. W. Plasschaert et al., Nature 560, 377 (2018). 10.1038/s41586-018-0394-6pmid:30069046
  7. J. Klaus et al., Nat. Med. 25, 561 (2019). 10.1038/s41591-019-0371-0pmid:30858616
  8. E. Villa et al., Curr. Op. Str. Biol. 23, 5 (2013). 10.1016/j.sbi.2013.08.006
  9. J. A. G. Briggs, Curr. Op. Str. Biol. 23, 2 (2013). 10.1016/j.sbi.2013.02.003
  10. T.A.Nakane et al., Nature 587, 152–156 (2020). 10.1038/s41586-020-2829-0
  11. 11. K. M. Yip et al., Nature 587, 157–161 (2020). 10.1038/s41586-020-2833-4