The University of Tokyo

Osaka University

Thinkcyte, Inc.

Riken Corporation

Thinkcyte K.k.

Issei Sato

Sadao Ota

Ryoichi Horisaki

Masashi Ugawa

Katsuhito Fujiu

Satoko Yamaguchi

Kayo Waki

Yoko Itahashi

Hiroaki Adachi

Yoko Kawamura

Masahiro Kazama

Fumiya Shimada

The present disclosure provides methods and systems for ghost cytometry (GC), which may be used to produce an image of an object without using a spatially resolving detector. This may be used to perform image-free ultrafast fluorescence 'imaging' cytometry, based on, for example, a single pixel detector. Spatial information obtained from the motion of cells relative to a patterned optical structure may be compressively converted into signals that arrive sequentially at a single pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random or pseudo-random pattern may permit computational reconstruction of cell morphology. Machine learning methods may be applied directly to the compressed waveforms without image reconstruction to enable efficient image-free morphology-based cytometry. Image-free GC may achieve accurate and high throughput cell classification as well as selective sorting based on cell morphology without a specific biomarker, which have been challenging using conventional flow cytometers.

Methods and systems for cytometry

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

Analysis device

Cytometry system and method for processing one or more target cells from a plurality of label-free cells

A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image. The sum total brightness calculation unit is configured to calculate the sum total brightness of the each pixel within the classification target image every time the movement unit relatively moves the mask by a predetermined movement amount. The mask structure optimization device further includes a mask pattern optimization unit that is configured to optimize the mask pattern of the mask on the basis of the sum total brightness.

Mask structure optimization device, mask structure optimization method, and program

Growing community of inventors

Tokyo, Japan

Issei Sato