Virtual-freezing fluorescence imaging flow cytometry

Abstract

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. For example, at high flow speed (i.e., high throughput), the integration time of the image sensor becomes short, resulting in reduced sensitivity or pixel resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually "freezing" the motion of flowing cells on the image sensor to effectively achieve 1,000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells/s without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

The in vitro micronucleus assay using imaging flow cytometry and deep learning

“…With the advancements in computing power over the last decade, the ability to capture imagery and apply deep learning networks in near-real time to physically sort cells of interest based on morphological features is now possible. Several such systems have been described, employing various methods of image capture including frequency-division-multiplexed microscopy 51 , photomultiplier tubes 52 , interdigital transducers 53 , Raman scattering 54 , and virtual-freezing fluorescence imaging 55,56 . While each of these methods demonstrates significant advances to sort cells based on fluorescence and morphological differences, applying these systems to sort all key events in the MN assay may be challenging due to the very subtle morphology of the MN and the high resolution, focused imagery required for precise detection.…”

High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

“…The trade-off between long exposure times (that reduce the noise but increase motion blur) and short exposure times (that reduce motion blur at the cost of increasing noise and reducing the number of collected photons) is unavoidable. According to Mikami et al the signal-to-noise ratio associated with a fluorescence image can be calculated using the signal-to-noise ratio of the camera readout per pixel (Mikami et al, 2020). The fluorescence signal (FS) expressed as the number of electrons is given by; S1.…”

Analysis of human chromosomes by imaging flow cytometry

“…The inclusion of imagery brings a new dimension to the analysis of chromosomes, as will be described. There are a number of imaging flow cytometry instruments available or in development, such as the Amnis ImageStream X markII, Amnis FlowSight, Sysmex MI‐1000, spectral resonance modulator (SRM) flow cytometer, and virtual‐freezing fluorescence imaging flow cytometer (Basiji et al, 2007; Huang et al, 2016; Mikami et al, 2020). Instrument configuration varies in the number of lasers (presently up to 6), fluorescence channels and the method of image and fluorescence capture.…”

“…Instrument configuration varies in the number of lasers (presently up to 6), fluorescence channels and the method of image and fluorescence capture. The types of imaging technologies include: Camera based imaging device which utilizes either charged couple device (CCD) or complementary metal‐oxide semiconductor, for example, ImageStreamX (Basiji et al, 2007) and virtual freezing imaging flow cytometry (Mikami et al, 2020). Photomultiplier (PMT) tube‐based imaging device which utilizes PMT combined with a technique named spatial–temporal transformation, for example, 3D imaging flow cytometry (Han & Lo, 2015).…”