Provided herein are optical systems and methods for detecting and characterizing particles. Systems and method are provided which increase the sensitivity of an optical particle counter and allow for detection of smaller particles while analyzing a larger fluid volume. The described systems and methods allow for sensitive and accurate detection and size characterization of nanoscale particles (e.g., less than 50 nm, optionally less than 20 nm, optionally less than 10 nm) for large volumes of analyzed fluids.

In one embodiment, a flow cytometer is disclosed having a compact light detection module. The compact light detection module includes an image array with a transparent block, a plurality of micro-mirrors in a row coupled to a first side of the transparent block, and a plurality of filters in a row coupled to a second side of the transparent block opposite the first side. Each of the plurality of filters reflects light to one of the plurality of micro-mirrors and passes light of a differing wavelength range and each of the plurality of micro-mirrors reflects light to one of the plurality of filters, such that incident light into the image array zigzags back and forth between consecutive filters of the plurality of filters and consecutive micro-mirrors of the plurality of micro-mirrors. A radius of curvature of each of the plurality of micro-mirrors images the fiber aperture onto the odd filters and collimates the light beam on the even filters.

This disclosure pertains to analytical instruments and related methods incorporating beam shaping optics for differentiating very bright and closely related signals over a wide range of operating conditions with an improved and uniform performance.

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.

A particle detection device includes: a first light source to emit first irradiation light; a first light-collection member; a second light-collection member facing the first reflection surface; a second light source to emit second irradiation light; and a first light-reception element. When the first light source emits the first irradiation light, the first light-reception element detects, as the first incident light, scattered light generated when a particle existing at a detection position in a target space is irradiated with the first irradiation light. When the second light source emits the second irradiation light, the first light-reception element detects, as the first incident light, a light ray of the second irradiation light that is reflected by the first reflection surface and a light ray of the second irradiation light that is reflected by both the first reflection surface and the second reflection surface.

The present application discloses a plurality of embodiments and associated inventions, with respect to microfluidic systems for at least one of identifying, imaging, orientating, and sorting particles, in particular, biological cells, and more particularly, X and Y sperm cells. In some embodiments, a module system with functional connectors is provided, each module being connected by a connector that can provide additional functionality aside from enabling fluid flow between modules. The present disclosure also is directed to microfluidic systems which include particle delivery tubes configured to orient particles (e.g., X and Y sperm cells), as well as microfluidic systems for generating a static, spatial patterns within the microfluidic channel.

Methods for classifying fluorescent flow cytometer data are provided. In some instances, methods include processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into distinct populations according to the relationship of data points to relevant threshold values. In embodiments, methods include determining a measure of spillover spreading by calculating spillover spreading coefficients and combining them in a spillover spreading matrix. In some embodiments, populations of fluorescent flow cytometer data are adjusted to subtract the magnitude of spillover spreading. In embodiments, spillover spreading adjusted populations are partitioned after potential partitions are evaluated relative to the threshold values. In embodiments, partitioned populations of fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. Systems and computer-readable media for classifying fluorescent flow cytometer data are also provided.

Methods, systems, and devices are disclosed for imaging particles and/or cells using flow cytometry. In one aspect, a method includes transmitting a light beam at a fluidic channel carrying a fluid sample containing particles; optically encoding scattered or fluorescently-emitted light at a spatial optical filter, the spatial optical filter including a surface having a plurality of apertures arranged in a pattern along a transverse direction opposite to particle flow and a longitudinal direction parallel to particle flow, such that different portions of a particle flowing over the pattern of the apertures pass different apertures at different times and scatter the light beam or emit fluorescent light at locations associated with the apertures; and producing image data associated with the particle flowing through the fluidic channel based on the encoded optical signal, in which the produced image data includes information of a physical characteristic of the particle.

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 apparatuses to detect particles in dense particle fields are described. A time varying signal is partitioned into a plurality of segments. Parameters are determined from the segments. The time varying signal is parsed into a plurality of individual particle signal components based on the plurality of parameters.