The present invention is directed to a method of identifying, isolating, and enabling downstream analysis of circulating tumor cells comprising contacting a blood or blood serum sample of a subject with a composition comprising a phospholipid ether analog bound to a luminescent molecule or a magnetic bead and subjecting the blood or blood serum sample of the subject to fluorescent microscopy, flow cytometry or magnetic isolation.

Provided is a gate region estimation program and the like that estimate a gate region using a learning model. This gate region estimation program causes a computer to execute processing of: acquiring a group of scatter diagrams including a plurality of scatter diagrams each different in a measurement item that are obtained from measurements by flow cytometry; inputting the group of scatter diagrams acquired to a learning model trained based on teaching data including a group of scatter diagrams and a gate region; and outputting an estimated gate region obtained from the learning model.

A blood cell analysis method and a blood cell analyzer are provided. In the method and analyzer, characteristic information of white blood cell fragments is obtained based on side scattered light information and fluorescence information, characteristic information of platelets is obtained based on forward scattered light information and fluorescence information and then a count value for the platelets is acquired based on the characteristic information of the platelets and the characteristic information of the white blood cell fragments. The present invention can avoid the influence of the white blood cell fragments on the platelet counting, thereby ensuring the accuracy of the platelet counting without increasing costs.

Aspects of the present disclosure include methods for characterizing particles of a sample in a flow stream. Methods according to certain embodiments include generating frequency-encoded fluorescence data from a particle of a sample in a flow stream; and calculating phase-corrected spatial data of the particle by performing a transform of the frequency-encoded fluorescence data with a phase correction component. In certain embodiments, methods include generating an image of the particle in the flow stream based on the phase-corrected spatial data. Systems having a processor with memory operably coupled to the processor having instructions stored thereon, which when executed by the processor, cause the processor to calculate phase-corrected spatial data from frequency-encoded fluorescence data of a particle a flow stream are also described. Integrated circuit devices (e.g., field programmable gate arrays) having programming for practicing the subject methods are also provided.

A holographic microscopy characterization (HMC) process for utilizing holographic video microscopy to provide an efficient, automated, label-free method of accurately identifying cell viability. Optical properties of a sample of cells are determined by HMC. The optical properties are compared to known samples or compared over time to observe changes in the optical properties, enabling identification of cells as viable or not viable, or as extra-cellular or degraded cellular materials.

Methods of detecting the efficacy and presence of a biologic drug which uses one or more 2A peptide in its manufacture and/or final pharmaceutical formulation.

The present invention includes methods to determine a risk of a cardiovascular disease (CVD) or a subclinical CVD (sCVD) in a subject, by detecting a subset of intermediate monocyte cell populations in the immune cells, wherein a percentage of the subsets as compared to a total intermediate monocyte cell population present in the isolated immune cells is determined; comparing a proportion of the subsets subset of intermediate monocyte cell populations determined with a statistical sample representative of a proportion of equivalent subsets in a total intermediate monocyte cell populations from a subject that does not have a cardiovascular disease; and determining that the subject has an increased risk for cardiovascular disease where the subject has a decrease in the certain subsets, has an increase in another subset, or both, as compared to the statistical sample.

According to various embodiments, there is provided a method for identifying clusters in a dataset, the method including: determining for each data point in the dataset, a plurality of parameters including a first parameter and a second parameter, the first parameter being a distance between the data point and a nearest other data point having a local density that is higher than a local density of the data point, and the second parameter being a function of the local density of the data point and the first parameter; running statistical tests on each of the first parameter and the second parameter across the dataset, to identify outliers of the first parameter and outliers of the second parameter; and designating each data point where both the first parameter and the second parameter are identified outliers, as a centre of a respective cluster.

Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods. Some such embodiments include receiving, from a particle analyzer, measurements for a first portion of particles associated with an experiment. Some embodiments also include generating a tree representing groups of related particles based at least in part on the measurements, wherein the tree includes at least three groups. Some embodiments also include generating a measure of relatedness between a first group and a second group of the tree based at least in part on the measurements. Some embodiments also include and configuring the particle analyzer to classify a subsequent particle associated with the experiment with the first group real-time, wherein the subsequent particle is not included in the first portion of particles. Some embodiments also include sorting the subsequent particle.

A computer-implemented method for data analysis comprises obtaining a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting the second histograms.