Multi‐ATOM: Ultrahigh‐throughput single‐cell quantitative phase imaging with subcellular resolution

Abstract

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost‐effective and label‐free cellular assays, which provides useful insights into understanding the biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single‐cell precision to define cell identities in a large and heterogeneous population of cells—hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multiplexed asymmetric‐detection time‐stretch optical microscopy (multi‐ATOM) that captures and processes quantitative label‐free single‐cell images at ultrahigh throughput without compromising subcellular resolution. We show that multi‐ATOM, based upon ultrafast phase‐gradient encoding, outperforms state‐of‐the‐art QPI in permitting robust phase retrieval at a QPI throughput of >10 000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi‐ATOM for large‐scale, label‐free, multivariate, cell‐type classification (e.g. breast cancer subtypes, and leukemic cells vs peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi‐ATOM could empower new strategies in large‐scale biophysical single‐cell analysis with applications in biology and enriching disease diagnostics.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

“…27 The use of parameters from QPI has recently been explored to assess shifts in population distributions of cell phase parameters, indicating altered phenotype, or to differentiate multiple bacteria species based on their single-cell profiling capability. 28,29 The effects of cell seeding density, 4 exposure to anticancer drugs, 30,31 and other influences on cell phenotype [32][33][34] have been robustly evaluated with QPI. Quantitative imaging and machine learning have the potential to save time, labor, and reduce human error in phenotypic profiling, which could help pathologists and scientists to accurately detect circulating tumor cells, 35 classify cancer cells, 36,37 evaluate the metastatic potential of cancer cells, 38 and assess cancer drug resistance.…”

Quantitative Phase Imaging Flow Cytometry for Ultra‐Large‐Scale Single‐Cell Biophysical Phenotyping

“…The detailed working principle can be referred to Ref. . In brief, multi‐ATOM effectively measures the local phase gradient induced by the imaged cells—resulting in intensity variation that can be measured through partial beam‐block on the Fourier plane of the imaged cells (i.e., the multi‐ATOM module in Fig.…”

“…Both the magnitude and direction of the 2D local phase gradient, and thus quantitative phase, are retrieved based on an optical module based on a simple assembly of free‐space and fiber optics components [i.e., multi‐ATOM module in Fig. a ] . In this module, a one‐to‐four de‐multiplexer splits the image‐encoded pulsed beam into four identical replicas, each of which is half‐blocked by a knife‐edge from four different orientations (right, left, top, and bottom).…”

“…Here we present an ultra‐large‐scale single‐cell biophysical phenotyping approach through a newly developed QPI platform, coined multiplexed asymmetric‐detection time‐stretch optical microscopy [ multi‐ATOM ] , to address the aforementioned challenges. This technique features with ultrafast QPI encoding that overcomes the speed limitation of classic QPI as well as the loss (and thus resolution) limitation of interferometric time‐stretch QPI.…”

starmapVR: immersive visualisation of single cell spatial omic data

“…To demonstrate starmapVR's applicability, we have provided five example data sets on starmapVR website based on previously published single-cell RNA-seq data (Zheng et al, 2017), flow cytometry data (The FlowCAP Consortium et al, 2013), a reconstructed 3D spatial organisation inferred from single cell RNA-seq (Ren et al, 2020), spatial transcriptomic data ( 10x Genomics) and single cell data with quantitative phase (QP) images (Lee, Lau, et al, 2019).…”