A practical guide to intelligent image-activated cell sorting.
Akihiro IsozakiHideharu MikamiKotaro HiramatsuShinya SakumaYusuke KasaiTakanori IinoTakashi YamanoAtsushi YasumotoYusuke OguchiNobutake SuzukiYoshitaka ShirasakiTaichiro EndoTakuro ItoKei HirakiMakoto YamadaSatoshi MatsusakaTakeshi HayakawaHideya FukuzawaYutaka YatomiFumihito AraiDino Di CarloAtsuhiro NakagawaYu HoshinoYoichiroh HosokawaSotaro UemuraTakeaki SugimuraYasuyuki OzekiNao NittaKeisuke GodaPublished in: Nature protocols (2019)
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.
Keyphrases
- single cell
- high throughput
- deep learning
- rna seq
- cell therapy
- flow cytometry
- high resolution
- induced apoptosis
- randomized controlled trial
- magnetic resonance imaging
- gene expression
- single molecule
- decision making
- oxidative stress
- machine learning
- big data
- cell cycle arrest
- optical coherence tomography
- photodynamic therapy
- bone marrow
- computed tomography
- image quality
- label free
- neural network
- copy number
- data analysis
- contrast enhanced