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Paschos, G.

Publications and source records attributed to Paschos, G..

2 recordsLinked to original sources

Enabling subcellular DESI-MSI for broad adoption: Acquisition, analysis, and application

Subcellular mass spectrometry imaging (MSI) has required specialized instrumentation and approaches. Additionally, data analysis has required expensive proprietary software, or code-based open-source tools. These barriers render spatial metabolomics less accessible than peer spatial omics techniques (transcriptomics, proteomics). Here we demonstrate subcellular MSI on standard commercial hardware, analyzed using code-free, open-source software. An inexpensive, fully reversible modification of a stock DESI source takes the platform past its typical 5-10 m pixels to routine 2x2 m pixels, and 1x1 m in proof-of-principle experiments. At a tissue-glass boundary imaged at 2x2 m, tissue-specific ions showed no measurable spillover onto the adjacent glass within the {+/-}4.4 m uncertainty of the edge position. To make these data interpretable, we extend MSI.EAGLE, an open-source, vendor-agnostic application with two new methods: i) Spatial UMAP preserves tissue architecture alongside subcellular detail, where conventional clustering breaks apart at high resolution; ii) Histology Fit performs landmark-free co-registration, and recovers synthetic misalignments of up to 8 m to within one pixel of a reference registration. Together, these enable previously high-burden spatial metabolomics experiments. In mouse brain tissue, we demonstrate genuine subcellular signal by resolving mitochondrial cardiolipin into perinuclear puncta, quantifying cytoplasmic enrichment across 6,073 cells. Cell identities are assigned to MSI pixels using histology-predicted transcriptomics, linking cell provenance to metabolism in 2D space directly. Finally, we recover cell-type-specific metabolomes consistent with the transcriptomic identities. This workflow allows cellular and subcellular spatial metabolomics linked to cell provenance, with only stock MSI hardware, open-source GUI-based software, and common histology techniques, putting it within reach of many researchers and laboratories.

molecular biology↗

Iso-relevance Functions - A Systematic Approach to Ranking Genomic Features by Differential Effect Size

It is common to measure large numbers of features to identify those differing between experimental conditions; for example using RNA-Seq to search for differentially expressed genes. Ranking by p-value allows for statistical control, but has well known issues: unreliability without many replicates; and significance of biologically irrelevant effect sizes. As a result prioritization is typically performed in conjunction with effect size; the canonical one being "fold-change" [Formula]. However fold-change has several issues: division by zero, sensitivity to small values in the denominator, insensitivity to magnitude (1 over 2 equals 100 over 200). To mitigate these problems adding 1 to all values is a widely used heuristic; which we show using real and simulated data is typically highly sub-optimal, while the value 20 is nearly optimal in all cases. From another point of view, adding a fixed "pseudocount" to all values is essentially re-defining effect size from fold-change to something else. To explore this further, we axiomatize the concept of effect size and use this mathematical framework to study the problem in general. We also present the remarkable finding that pseudocounts strike a balance between sorting by fold-change and sorting by difference. Therefore, optimization is equivalent to finding the most harmonious balance between these two extremes. Lastly, the framework is illustrated on a fundamentally different type of problem, that of ranking di-codons by their differential abundance in the ORFeome of different species, where p-values are unavailable and one must solve the problem directly with effect sizes.

bioinformatics↗