公开(公告)号：US20250044212A1

公开(公告)日：2025-02-06

申请号：US18800710

申请日：2024-08-12

Applicant: ThinkCyte K.K.

Inventor： Toru Imai ,  Keiji Nakagawa

IPC: G01N15/1409 ,  G01N15/14 ,  G01N15/1429

Abstract: A flow cytometer includes a microfluidic device, a light source, a photodetector, an information generation device, a calculation device, and a flow path position control device. In the microfluidic device, a first position detection line is arranged on a flow path, a second position detection line is arranged with a portion overlapping the first position detection line in a width direction, and a position detection distance, which is a distance between the first position detection line and the second position detection line in a length direction of the flow path, changes with a position in the width direction. The calculation device includes a time difference calculation unit configured to calculate a time difference between the time when the photodetector has detected a peak intensity of an optical signal at any one detection position on the first position detection line and the time when the photodetector has detected the peak intensity of the optical signal at any one detection position on the second position detection line and a position calculation unit configured to calculate the position of the observation object in the width direction on the basis of the time difference and a corresponding relationship between the time difference and the position in the width direction.

公开(公告)号：US12235202B2

公开(公告)日：2025-02-25

申请号：US17847478

申请日：2022-06-23

Applicant: ThinkCyte K.K.

Inventor： Keiji Nakagawa

IPC: G01N15/14 ,  G01N15/10 ,  G01N15/149

Abstract: A method of evaluating performance of a flow cytometer configured to use a combination of two or more types of calibration particles having different morphologies from each other, includes a first classification step of classifying the calibration particles from each other based on a first optical characteristic by the flow cytometer which is an evaluation target, a second classification step of classifying the calibration particles from each other based on a second optical characteristic which is classifiable at a spatial resolution lower than a spatial resolution at which the first optical characteristic is classified, and the evaluation step of evaluating one or both of particle classification performance and a resolution of the flow cytometer based on a first classification result assessed in the first classification step and a second classification result assessed in the second classification step.

公开(公告)号：US20240310268A1

公开(公告)日：2024-09-19

申请号：US18603244

申请日：2024-03-13

Applicant: THINKCYTE K.K. ,  SEIKO EPSON CORPORATION

Inventor： Junichi OKAMOTO ,  Tatsunori MIYAZAWA ,  Keigo YAMASAKI

IPC: G01N15/1434 ,  G01N15/10

CPC classification number: G01N15/1404 ,  G01N2015/1006

Abstract: An observation chip 1 includes: a plurality of substrates 10 stacked in a thickness direction Z of the substrates; a first channel 25 extending along a first axis O1 along the substrates; a surrounding channel 30 surrounding the first channel all around in a cross section orthogonal to the first axis in the first channel from an end of the first channel to an overall merging point P2 of the first channel, integrated with the first channel, and forming a second channel 55 that extends along a second axis O2 along the substrates; and an observation section 60 provided on a side farther away from the first channel than the overall merging point in the second channel, and transmitting electromagnetic waves from outside to the second channel. A length of the second channel in the thickness direction is constant from the overall merging point to the observation section.

公开(公告)号：US12259311B2

公开(公告)日：2025-03-25

申请号：US18238368

申请日：2023-08-25

Applicant: ThinkCyte K.K. ,  University of Tokyo ,  Osaka University

Inventor： Sadao Ota ,  Ryoichi Horisaki ,  Yoko Kawamura ,  Masashi Ugawa ,  Issei Sato

IPC: G01N15/14 ,  G01N15/1434 ,  G01N15/10 ,  G01N15/149

Abstract: 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.

公开(公告)号：US20240280467A1

公开(公告)日：2024-08-22

申请号：US18571582

申请日：2022-06-17

Applicant: ThinkCyte K.K. ,  The University of Tokyo

Inventor： Hiroaki Adachi ,  Sadao Ota

IPC: G01N15/1433 ,  G01N15/149 ,  G06V10/764

CPC classification number: G01N15/1433 ,  G01N15/149 ,  G06V10/764

Abstract: First waveform data, which indicates the morphological characteristics of a particle and is obtained by emitting light to the particle, and a photographic image obtained by photographing the particle are acquired. A first trained model that outputs a particle image when waveform data is input is generated by training using first training data including the first waveform data and the photographic image. Second waveform data is input to the first trained model. Classification information indicating a classification into which the particle is classified according to the morphological characteristics is acquired in association with the particle image output from the first trained model. Data including the second waveform data and the classification information is stored as second training data for training a second trained model that outputs classification information when waveform data is input.

公开(公告)号：US20240241031A1

公开(公告)日：2024-07-18

申请号：US18688948

申请日：2022-08-29

Applicant: ThinkCyte K.K. ,  The University of Tokyo

Inventor： Hiroaki Adachi ,  Issei Sato

IPC: G01N15/1429 ,  G01N15/01 ,  G01N15/10 ,  G01N15/1434 ,  G01N15/149

CPC classification number: G01N15/1429 ,  G01N15/01 ,  G01N15/1434 ,  G01N15/149 ,  G01N2015/1006

Abstract: In the classification model generation method, for each particle contained in a first sample containing a mixture of particles having specific morphological characteristics and other particles and a second sample that does not contain particles having the specific morphological characteristics but contains only the other particles, observation data indicating a result of observing the particle is acquired. By training using training data including the observation data and information indicating whether the observation data has been obtained from a particle contained in the first sample or the second sample, a classification model is generated that outputs discrimination information indicating whether or not a particle has the specific morphological characteristics when the observation data indicating a result of observing the particle is input.

公开(公告)号：US20240133792A1

公开(公告)日：2024-04-25

申请号：US18238368

申请日：2023-08-25

Applicant: ThinkCyte K.K. ,  University of Tokyo ,  Osaka University

Inventor： Sadao Ota ,  Ryoichi Horisaki ,  Yoko Kawamura ,  Masashi Ugawa ,  Issei Sato

IPC: G01N15/14

CPC classification number: G01N15/1434 ,  G01N15/1459 ,  G01N2015/1006

Abstract: 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.

公开(公告)号：US20250044211A1

公开(公告)日：2025-02-06

申请号：US18800748

申请日：2024-08-12

Applicant: ThinkCyte K.K.

Inventor： Toru Imai ,  Hiroaki Adachi

IPC: G01N15/1404 ,  G01N15/14 ,  G01N15/1434

Abstract: A flow cytometer includes a microfluidic device, a light source, a photodetector that detects, in time series, the intensity of signal light emitted from an observation object when the observation object flowing through a flow path is irradiated with illumination light, an information generation device that generates optical information indicating a structure of the observation object based on the intensity of the signal light, an arithmetic device, and a flow path position control device, wherein the arithmetic device includes a signal intensity acquiring unit that acquires electronic data of temporal changes in the intensity of the signal light detected based on a detection position predetermined in the flow path to detect the depth position in the flow path where the observation object passes through the flow path, a scan unit that performs a scan process to move the flow path in the depth direction and acquire the electronic data at different depth positions, a position calculation unit that calculates the depth position based on the electronic data, and an output unit that outputs position information indicating the depth position calculated by the position calculation unit.

公开(公告)号：US20240410810A1

公开(公告)日：2024-12-12

申请号：US18700652

申请日：2021-10-15

Applicant: ThinkCyte K.K.

Inventor： Toru Imai ,  Keiji Nakagawa

IPC: G01N15/1409 ,  G01N15/14 ,  G01N15/1429

Abstract: A flow cytometer includes a microfluidic device, a light source, a photodetector, an information generation device, a calculation device, and a flow path position control device. In the microfluidic device, a first position detection line is arranged on a flow path, a second position detection line is arranged with a portion overlapping the first position detection line in a width direction, and a position detection distance, which is a distance between the first position detection line and the second position detection line in a length direction of the flow path, changes with a position in the width direction. The calculation device includes a time difference calculation unit configured to calculate a time difference between the time when the photodetector has detected a peak intensity of an optical signal at any one detection position on the first position detection line and the time when the photodetector has detected the peak intensity of the optical signal at any one detection position on the second position detection line and a position calculation unit configured to calculate the position of the observation object in the width direction on the basis of the time difference and a corresponding relationship between the time difference and the position in the width direction.

公开(公告)号：US20240362454A1

公开(公告)日：2024-10-31

申请号：US18648220

申请日：2024-04-26

Applicant: ThinkCyte K.K.

Inventor： Hirofumi Nakayama ,  Ryo Tamoto

IPC: G06N3/042

CPC classification number: G06N3/042

Abstract: Systems and methods are provided to implement classification of objects, based on sensor data regarding the objects, in a manner that addresses variations in the sensor data, including measurement variables among the objects. A system can include one or more processors to retrieve sensor data regarding an object that is at least one of cellular material from one or more cells, nucleic acid material, biological material, or chemical material. The one or more processors can apply the sensor data as input to a classifier to cause the classifier to determine a classification of the object, the classifier configured based on feature data from a first example of object data and a second example of object data associated with at least one of a different time of detection or a different subject than the first example.