Detecting protein and post-translational modifications in single cells with iDentification and qUantification sEparaTion (DUET)

Picosecond laser capture microdissection based on edge catapulting combined with dielectrophoretic force

The spatial omics information analysis of heterogeneous cells or cell populations is of great importance for biomedical research. Herein, we proposed a picosecond laser capture microdissection boosted by edge catapulting combined with dielectrophoretic force (ps-LMED) that enables fast and non-invasive acquisition of uncontaminated cells and cell populations for downstream molecular assays. The target cells were positioned under a microscope and separated by a focused picosecond pulsed laser. The system employed the plasma expansion force during cutting to lift the target and captured it under dielectrophoretic force from the charged collection cap eventually. The principle of our system has been validated by both theoretical analysis and practical experiments. The results indicated that our system can collect samples ranging from a single cell with a diameter of a few microns to large tissues with a volume of 532,500 µm3 at the moment finishing the cutting, without further operations. The cutting experiments of living cells and ribonucleic acid (RNA) and protein omics analysis results of collected targets demonstrated the advantage of non-destructiveness to the samples and feasibility in omics applications.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

IQSEC2-related encephalopathy in male children: Novel mutations and phenotypes

The isoleucine–glutamine (IQ) motif and Sec7 domain-containing protein 2 (IQSEC2) gene, located at Xp11. 2, are associated with nervous system diseases, such as epilepsy, autism, and intellectual disabilities. Gender-related differences in the severity of phenotype severity have been described previously. Here, we report the details of seven male children with IQSEC2 mutations from different families. During this investigation, we explored the relationship between the genotype and phenotype of IQSEC2 mutations; to do so, we recruited seven children with pathogenic/likely pathogenic IQSEC2 mutations who were diagnosed with global developmental delay and/or epilepsy. Their clinical features were assessed, and Trio-based whole-exome sequencing (trio WES) was conducted in seven pedigrees. A variety of algorithms and computational tools were used to calculate the pathogenicity, protein stability, conservation, side chain properties, and protein-protein interactions of mutated proteins. The seven patients ranged in age from 18 months to 5 years. Among them, six children were found to have both developmental delay and epilepsy, and one child only exhibited developmental delay. Four novel mutations (c.316C > T, c.443_4 44dup, c.3235T > C, and c.1417G > T) were newly reported. Two patients did not have truncated aberrant proteins caused by missense mutations. Still, they did have severe phenotypes, such as early-onset epilepsy in infancy, because the mutations were located in domains like the pleckstrin homology and IQ calmodulin-binding motif domains. The bioinformatics analysis also proved that missense mutations may be located in the functional region, which affects protein stability and is harmful. In summary, severe phenotypes, such as early-onset epilepsy in infancy, occur in male patients with a missense mutation in specific domains (e.g., pleckstrin homology and IQ calmodulin-binding motif domains). Some female individuals with IQSEC2 mutations may be asymptomatic because of the skewed inactivation of the X chromosome.

Segmentation-based analysis of single-cell immunoblots

From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single-cell biology. Detection of specific proteoforms-with single-cell resolution-presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single-cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low-abundance, or low-SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation-based approaches to robustly quantify protein bands from single-cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low-abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning-based segmentation approaches increases the recovery of low-SNR protein bands by an additional 50%. However, we find that segmentation-based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, Rs < 0.6). With burgeoning needs for more single-cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation-based analysis approaches.