Enhancing flow cytometry discrimination with geometric transformation

Abstract

In flow cytometry, particles ( 2 ) can be distinguished between populations ( 8 ) by combining n-dimensional parameter data, which may be derived from signal data from a particle, to mathematically achieve numerical results representative of an alteration ( 48 ). An alteration may include a rotational alteration, a scaled alteration, or perhaps even a translational alteration. Alterations may enhance separation of data points which may provide real-time classification ( 49 ) of signal data corresponding to individual particles into one of at least two populations.

We claim: 
     
       1. A method of operating a flow cytometry apparatus with at least n detectors to analyze at least two populations of particles in the same sample, the method comprising:
 (a) establishing a fluid stream in the flow cytometry apparatus with at least n detectors, the at least n detectors including a first detector and a second detector; 
 (b) entraining particles from the sample in the fluid stream in the flow cytometry apparatus; 
 (c) executing instructions read from a computer readable memory with a processor, the processor being in communication with the first detector in the flow cytometer, to detect a first signal from the first detector based on individual particles in the fluid stream; 
 (d) executing instructions read from the computer readable memory with the processor, the processor being in communication with the second detector in the flow cytometer, to detect a second signal from the second detector based on the individual particles in the fluid stream; 
 (e) executing instructions read from the computer readable memory with the processor to convert at least the first signal and the second signal into n-dimensional parameter data for detected particles in the sample, wherein the n-dimensional parameter data for particles from the at least two populations overlap in at least one of the dimensions; 
 (f) executing instructions read from the computer readable memory with the processor to rotationally alter the n-dimensional parameter data so that spatial separation of the data from the particles from the at least two populations in the at least one dimension that is overlapped is increased; 
 (g) executing instructions read from the computer readable memory with the processor to real-time classify each of the individual detected particles into one of a first population and a second population of the at least two populations based on at least the rotationally altered n-dimensional parameter data; and 
 (h) using the real-time classification, sorting the individual particles with the flow cytometer. 
 
     
     
       2. The method of  claim 1 , further comprising:
 (a) executing instructions with the processor read from the computer readable memory to create a first dimensional altered data point by calculating a first 1st-dimensional alteration value times a 1 st-dimensional data point summed with a second 1 st-dimensional alteration value times a 2nd-dimensional data point summed with a third 1 st-dimensional alteration value times a 3rd-dimensional data point; and 
 (b) executing instructions with the processor read from the computer readable memory to create a second dimensional altered data point by calculating a first 2nd-dimensional alteration value times the 1 st-dimensional data point summed with a second 2nd-dimensional alteration value times the 2nd-dimensional data point summed with a third 2nd-dimensional alteration value times the 3rd-dimensional data point. 
 
     
     
       3. The method of  claim 1 , wherein the rotational alteration increases discrimination between the first population and the second population. 
     
     
       4. The method of  claim 1 , further comprising:
 (a) altering the n-dimensional parameter data by scaling the n-dimensional parameter data; and 
 (b) real-time classifying the n-dimensional parameter data of each of the individual particles into one of the at least two populations based on at least the scaled n-dimensional parameter data. 
 
     
     
       5. The method of  claim 4 , further comprising
 (a) executing instructions with the processor read from the computer readable memory to create a first dimensional altered data point by calculating a first 1st-dimensional alteration value times a 1 st-dimensional data point summed with a second 1 st-dimensional alteration value times a 2nd-dimensional data point summed with a third 1 st-dimensional alteration value times a 3rd-dimensional data point; and 
 (b) executing instructions read from the computer readable memory with the processor to create a second dimensional altered data point by calculating a first 2nd-dimensional alteration value times the 1 st-dimensional data point summed with a second 2nd-dimensional alteration value times the 2nd-dimensional data point summed with a third 2nd-dimensional alteration value times the 3rd-dimensional data point. 
 
     
     
       6. The method of  claim 5 , wherein the alteration values are based on a zoom and tracking element and the alteration increases discrimination between the first population and the second population in the n-dimensional parameter data. 
     
     
       7. The method of  claim 4 , further comprising:
 executing instructions read from the computer readable memory with the processor to simultaneously rotationally alter and scale the n-dimensional parameter data. 
 
     
     
       8. The method of  claim 1  or  4 , further comprising:
 (a) executing instructions read from the computer readable memory with the processor to alter the n-dimensional parameter data by translating the n-dimensional parameter data; and 
 (b) executing instructions read from the computer readable memory with the processor to real-time classify each of the individual particles into one of said the first population and the second population based on at least the translated n-dimensional parameter data. 
 
     
     
       9. The method of  claim 8 , further comprising:
 (a) executing instructions read from the computer readable memory with the processor to create a first dimensional altered data point by calculating a first 1st-dimensional alteration value times a 1 st-dimensional data point summed with a second 1 st-dimensional alteration value times a 2nd-dimensional data point summed with a third 1 st-dimensional alteration value times a 3rd-dimensional data point; and 
 (b) executing instructions read from the computer readable memory with the processor to create a second dimensional altered data point by calculating a first 2nd-dimensional alteration value times the 1 st-dimensional data point summed with a second 2nd-dimensional alteration value times the 2nd-dimensional data point summed with a third 2nd-dimensional alteration value times the 3rd-dimensional data point. 
 
     
     
       10. The method of  claim 9 , wherein the alteration values comprise translating of the n-dimensional parameter data with respect to a center point of rotation. 
     
     
       11. The method of  claim 9 , wherein the alteration values are based on an operation selected from a group consisting of rotation in x-axis, rotation in y-axis, rotation in z-axis, translation, scale, and perspective operations. 
     
     
       12. The method of  claim 8 , further comprising:
 executing instructions read from the computer readable memory with the processor to simultaneously rotationally alter, scale, and translate the n-dimensional parameter data. 
 
     
     
       13. The method of  claim 1 , further comprising:
 executing instructions read from the computer readable memory with the processor to cause a display device to display the n-dimensional parameter data for each of the individual particles in relation to the at least two populations. 
 
     
     
       14. The method of  claim 1 , further comprising:
 executing instructions read from the computer readable memory with the processor to cause a display device to display the n-dimensional parameter data in a Cartesian coordinate system. 
 
     
     
       15. The method of  claim 14 , further comprising:
 executing instructions read from the computer readable memory with the processor to cause the display device to display: 
 (a) the first signal on a first axis; and 
 (b) the second signal on a second axis. 
 
     
     
       16. The method of  claim 1 , further comprising:
 executing instructions read from the computer readable memory with the processor to cause a display device to display a histogram of the n-dimensional parameter data. 
 
     
     
       17. The method of  claim 1 , further comprising:
 executing instructions read from the computer readable memory with the processor to cause a display device to display: 
 (a) elliptical shaped populations of each of the first population and the second population, the displayed elliptical shaped populations having non-orthogonal angles of inclination to at least one axis; and 
 (b) the elliptical shaped populations orthogonal to at least one axis. 
 
     
     
       18. The method of  claim 1 , wherein:
 (a) the particles include sperm; and 
 (b) the first population includes an X-bearing sperm population; and 
 (c) the second population includes a Y-bearing sperm population. 
 
     
     
       19. The method of  claim 1 , wherein the first signal and the second signal comprises fluorescence emitted from a light emitting element coupled with the individual particles after passing through a laser beam. 
     
     
       20. The method of  claim 1 , further comprising:
 identifying a point situated between the first population and second population when the n-dimensional parameter data for a first plurality of particles from the first population and a second plurality of particles from the second population is plotted on a Cartesian coordinate system, such that the point is positioned so that the separation of the first population and second population is increased with respect to one axis of the Cartesian coordinate system when the n-dimensional parameter data for the first and second plurality of particles is rotated about the point, and 
 wherein rotationally altering the n-dimensional parameter data includes rotating the n-dimensional parameter data about the point. 
 
     
     
       21. The method of  claim 1 , wherein the processor is a digital signal processor. 
     
     
       22. The method of  claim 21 , wherein the digital signal processor is a fixed point processor. 
     
     
       23. The method of  claim 1  wherein the instructions are assembly language code.