Biology subjects

Psaltis, D.

Publications and source records attributed to Psaltis, D..

4 recordsLinked to original sources

bioRxiv2026

Label-Free Nucleoli Measurement by 3D Holo-Tomographic Flow Cytometry Using Biolens Phase Compensation

Holo-Tomographic Flow Cytometry (HTFC) holds the potential to transform cellular research and clinical screening through 3D label-free quantitative phase imaging (QPI) of flowing single cells. However, it has been limited by insufficient intracellular specificity in 3D refractive index (RI) distributions, since suspended cells act as highly aberrating spherical biolenses obscuring internal structures. Here, we show the Biolens Phase Compensation (BPC), a method that corrects phase aberrations in 2D QPI projections to transform the 3D RI tomogram. Working within this new 3D pseudo-RI space demonstrates for the first time the extraction of nucleoli in HTFC. Extensive validation against 2D fluorescence flow cytometry and 3D confocal microscopy demonstrates that BPC achieves reliable intranuclear specificity. Using statistically significant single-cell analysis, we provide multiplexed quantitative 3D measurements of nested intracellular compartments (cytoplasm, nucleoplasm, nucleoli). This approach extends label-free HTFC toward capabilities of gold-standard Fluorescence Microscopy, overcoming its well-known drawbacks in intracellular and intranuclear staining.

biophysics↗

bioRxiv2025

Single-cell phenomics through integrated imaging and molecular profiling

Single-cell technologies such as transcriptomics, microscopy, and flow cytometry have revolutionized the study of cellular identity and function. While each of these technologies is powerful on its own, their full potential lies in their integration, enabling multimodal profiling of the same cell and revealing how distinct modalities influence one another. Here, we introduce IRIS (Interconnected Robotic Imaging and Single cell transcriptomics), a deterministic single-cell platform technology that seamlessly couples high-resolution microscopy with droplet-based single-cell RNA sequencing. IRIS enables precise cell positioning, multimode imaging across brightfield and fluorescent channels, and subsequent molecular capture from the same cell, directly linking high-resolution morphological features to matched transcriptomes. We validate IRIS by recovering cell cycle progression states and transcriptional programmes associated with canonical morphologies and demonstrate its discovery power by molecularly resolving two nuclear-ER architectures within naive CD8+ T cells, each defined by distinct gene expression profiles and functional markers. IRIS establishes an integrative single-cell phenomics framework, opening new avenues for dissecting how cellular form relates to molecular state and function.

immunology↗

bioRxiv2023

An actor-model framework for visual sensory encoding

A fundamental challenge in neuroengineering is determining a proper input to a sensory system that yields the desired functional output. In neuroprosthetics, this process is known as sensory encoding, and it holds a crucial role in prosthetic devices restoring sensory perception in individuals with disabilities. For example, in visual prostheses, one key aspect of image encoding is to down-sample the images captured by a camera to a size matching the number of inputs and resolution of the prosthesis. Here, we show that down-sampling an image using the inherent computation of the retinal network yields better performance compared to a learning-free down-sampling encoding. We validated a learning-based approach (actor-model framework) that exploits the signal transformation from photoreceptors to retinal ganglion cells measured in explanted retinas. The actor-model framework generates down-sampled images eliciting a neuronal response in-silico and ex-vivo with higher neuronal reliability to the one produced by original images compared to a learning-free approach (i.e. pixel averaging). In addition, the actor-model learned that contrast is a crucial feature for effective down-sampling. This methodological approach could serve as a template for future image encoding strategies. Ultimately, it can be exploited to improve encoding strategies in visual prostheses or other sensory prostheses such as cochlear or limb.

bioengineering↗

bioRxiv2021

Stain-free nucleus identification in holographic learning flow cyto-tomography

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

bioengineering↗