Methods for sorting particles

• US 8,198,093 B2
• Filed: 07/12/2011
• Issued: 06/12/2012
• Est. Priority Date: 03/28/2003
• Status: Active Grant

• Current Assignee: Inguran LLC
• Original Assignee: Inguran LLC
• Inventors: Durack, Gary, Wallace, Jeffrey D., Vandre, Gary P., Westfall, Lon A., Hatcher, Jeremy T., Nayak, Niraj V.
• Primary Examiner(s): Wallenhorst, Maureen
• Application Number:
  US13/181,150
• Publication Number:
  US 20110269175A1
• Time in Patent Office: 336 Days
• Field of Search: 422/67, 422/68.1, 422/73, 422/82.05, 422/82.08, 436/63, 436/164, 436/165, 436/43, 436/50, 436/55, 436/172, 435/2, 435/287.1, 435/286.1, 435/366, 435/29, 435/288.7, 702/19, 702/21
• US Class Current: 436/63
• CPC Class Codes: A01N 1/0284 Temperature processes, i.e. using a designated change in temperature over time
• CPC Class Codes: C12N 5/06 Animal cells or tissues; Human cells or tissues preservation of living cells or tissues A01N1/02
• CPC Class Codes: C12N 5/061 Sperm cells, spermatogonia
• CPC Class Codes: C12N 5/0612 sorting of gametes, e.g. according to sex or motility
• CPC Class Codes: C12Q 1/02 involving viable microorganisms
• CPC Class Codes: C12Q 3/00 Condition responsive control processes apparatus therefor C12M1/36; controlling or regulating in general G05
• CPC Class Codes: G01N 15/01 specially adapted for biological cells, e.g. blood cells investigating sedimentation of particle suspensions in blood G01N15/05
• CPC Class Codes: G01N 15/1404 Handling flow, e.g. hydrodynamic focusing
• CPC Class Codes: G01N 15/1459 the analysis being performed on a sample stream
• CPC Class Codes: G01N 15/1468 with spatial resolution of the texture or inner structure of the particle
• CPC Class Codes: G01N 15/147 the analysis being performed on a sample stream
• CPC Class Codes: G01N 15/149 specially adapted for sorting particles, e.g. by their size or optical properties
• CPC Class Codes: G01N 2015/1006 for cytology
• CPC Class Codes: G01N 2015/1406 Control of droplet point
• CPC Class Codes: G01N 2015/1415 Control of particle position
• CPC Class Codes: G01N 2021/6439 with indicators, stains, dyes, tags, labels, marks
• CPC Class Codes: G01N 21/63 optically excited
• CPC Class Codes: G01N 21/6428 Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" in vivo A61B5/00; immunoassay G01N33/53
• CPC Class Codes: G01N 33/48 Biological material, e.g. blood, urine G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence; Haemocytometers counting blood corpuscules distributed over a surface by scanning the surface G06M11/02
• CPC Class Codes: G01N 33/5005 involving human or animal cells immunoassay G01N33/56966; immunoassays of protozoa G01N33/56905; protozoa in screening assays C12Q1/025
• CPC Class Codes: Y10T 436/10 : Composition for standardization, calibration, simulation, stabilization, preparation or preservation; processes of use in preparation for chemical testing
• CPC Class Codes: Y10T 436/115831 : Condition or time responsive
• CPC Class Codes: Y10T 436/12 : Condition responsive control
• CPC Class Codes: Y10T 436/25 : including sample preparation
• CPC Class Codes: Y10T 436/2525 : Stabilizing or preserving
• CPC Class Codes: Y10T 436/25375 : Liberation or purification of sample or separation of material from a sample [e.g., filtering, centrifuging, etc.]
• CPC Class Codes: Y10T 436/255 : including use of a solid sorbent, semipermeable membrane, or liquid extraction
• CPC Class Codes: Y10T 436/2575 : Volumetric liquid transfer

Abstract

A method of sorting stained particles in a flow cytometer by generating a mathematical model based upon extracted features of waveform pulses produced in response to the stained particles. Waveform pulses can then be analyzed to determine the probability the stained particles belong in either a first or a second population and a decision boundary can be established. Sorting decisions can be made based upon the extracted features of the waveform pulses relative to the decision boundary in combination with other desirability factors for particles or droplets containing the particles.

Claims

1. A method of sorting particles in a flow cytometer comprising the steps of:

a. establishing a mathematical model based upon extracted features of waveform pulses produced by stained particles when run through the flow cytometer, wherein the particles flowing through the flow cytometer belong to at least a first population or a second population, wherein the mathematical model represents a statistical distribution of extracted features of the waveform pulses;

b. analyzing the waveform pulses produced by stained particles flowing through the flow cytometer to determine a probability each particle belongs to the first population and the second population based upon the mathematical model;

c. assigning each stained particle a value based upon the probabilities associated with belonging to the first population or the second population;

d. establishing a decision boundary;

e. modifying the value assigned to each stained particle relative to the decision boundary based upon a purity or quantity of the first population or of the second population; and

f. separating the stained particles based upon their modified relationship relative to the decision boundary.

2. The method according to claim 1 wherein the mathematical model comprises either a sum of multiple components having uni-variate Gaussian distributions of an extracted feature or a sum of multiple components having bi-variate Gaussian distributions of extracted features.

3. The method according to claim 2 wherein the mathematical model comprises one selected from the group consisting of:

two components having Gaussian distributions, three components having Gaussian distributions; and

four components having Gaussian distributions.

4. The method according to claim 1 wherein the stained particles comprise stained sperm cells.

5. The method according to claim 4 wherein the first population of stained sperm cells comprises aligned X-chromosome bearing sperm cells and the second population of stained sperm cells comprises aligned Y-chromosome bearing sperm cells.

6. The method according to claim 5 further comprising:

at least a third group of stained sperm cells, wherein the third group of stained sperm cells comprises unaligned X- and Y-chromosome bearing sperm cells.

7. The method according to claim 5 further comprising at least a third group of stained sperm cells and a fourth group of stained sperm cells, wherein the third group comprises unaligned X-chromosome bearing sperm cells and the fourth group comprises unaligned Y-chromosome bearing sperm cells.

8. The method according to claim 4 wherein only pulses having pulse widths indicative of a single X- or Y-chromosome bearing sperm are considered.

9. The method of sorting particles according to claim 1 wherein the waveform pulses are generated by light emitted from the stained particles in response to an excitation with electromagnetic radiation.

10. The method of sorting particles according to claim 9 wherein the extracted features include a peak height and a peak area of the waveform pulses produced by stained particles.

11. The method of sorting particles according to claim 10 wherein the step of establishing a decision boundary further comprises establishing a boundary at which the probability of a stained particle belonging to the first population is equal to the probability of the stained particle belonging to the second population.

12. The method of sorting particles according to claim 10 wherein the stained particles comprise stained sperm cells and the extracted feature of the peak height of the associated pulse waveform and the extracted feature of the peak area of the pulse waveform are compared against the mathematical model to determine the probability a sperm cell is an X-chromosome bearing sperm cell.

13. A method of sorting particles in a flow cytometer comprising the steps of:

a. establishing a mathematical model based upon extracted features of waveform pulses produced by stained particles when the flow cytometer, wherein the particles flowing through the flow cytometer belong to at least a first population or a second population, wherein the mathematical model represents a statistical distribution of extracted features of the waveform pulses;

b. analyzing the waveform pulses produced by stained particles flowing through the flow cytometer to determine a probability each particle belongs to the first population and the second population based upon the mathematical model;

c. assigning each stained particle a value based upon the probabilities associated with belonging to the first population or the second population;

d. establishing a decision boundary;

e. modifying the value assigned to each stained particle relative to the decision boundary, wherein the modification of the value assigned to each particle is related to a cost associated with misidentifying a particle as belonging to the first population or the second population; and

f. separating the stained particles based upon their modified relationship relative to the decision boundary.

14. The method according to claim 13 wherein the cost is varied in order to establish high purity sorting or high throughput sorting.

15. A method of sorting particles in a flow cytometer comprising the steps of:

a. establishing a mathematical model based upon extracted features of waveform pulses produced by stained particles when run through the flow cytometer, wherein the particles flowing through the flow cytometer belong to at least a first population or a second population wherein the mathematical model represents a statistical distribution of extracted features of the waveform pulses;

b. analyzing the waveform pulses produced by stained particles flowing through the flow cytometer to determine a probability each particle belongs to the first population and the second population based on the mathematical model;

c. entraining the stained particles in droplets and determining the desirability of a droplet containing one or more of the stained particles;

d. establishing a decision boundary relating to at least one extracted feature of the waveform pulse for a stained particle; and

e. collecting droplets based upon the relationship of the one or more stained particles within each droplet relative to the decision boundary and based upon the determined desirability of the droplet.

16. The method according to claim 15 wherein the mathematical model comprises a sum of multiple components having uni-variate Gaussian distributions of an extracted feature or a sum of multiple components having bi-variate Gaussian distributions of extracted features.

17. The method according to claim 16 wherein the mathematical model comprises one selected from the group consisting of:

two components having Gaussian distributions, three components having Gaussian distributions; and

four components having Gaussian distributions.

18. The method according to claim 15 wherein the stained particles comprise stained sperm cells.

19. The method according to claim 18 wherein the first population of stained sperm cells comprises aligned X-chromosome bearing sperm cells and the second population of stained sperm cells comprises aligned Y-chromosome bearing sperm cells.

20. The method according to claim 19 further comprising:

at least a third group of stained sperm cells, wherein the third group of stained sperm cells comprises unaligned X- and Y-chromosome bearing sperm cells.

21. The method according to claim 19 further comprising at least a third group of stained sperm cells and a fourth group of stained sperm cells, wherein the third group comprises unaligned X-chromosome bearing sperm cells and the fourth group comprises unaligned Y-chromosome bearing sperm cells.

22. The method of sorting particles according to claim 15 wherein the waveform pulses are generated by light emitted from the stained particles in response to an excitation with electromagnetic radiation.

23. The method of sorting particles according to claim 22 wherein the extracted features include a peak height and a peak area of the waveform pulses produced by stained particles.

24. The method of sorting particles according to claim 23 wherein the stained particles comprise stained sperm cells and the extracted feature of the peak height of the associated pulse waveform and the extracted feature of the peak area of the pulse waveform are compared against the mathematical model to determine the probability a sperm cell is an X-chromosome bearing sperm cell.

25. The method of sorting particles according to claim 15 wherein the step of establishing a decision boundary further comprises establishing a boundary at which the probability of a stained particle belonging to the first population is equal to the probability of the stained particle belonging to the second population.

26. The method according to claim 15 wherein the step of determining the desirability of a droplet containing one or more of the stained particles further comprises the step of determining whether a coincident particle exists within the droplet.

27. The method of claim 26 wherein the existence of a coincident particle is determined with the time, velocity and position of particles relative to forming droplets.

28. The method according to claim 15 wherein the step of determining the desirability of a droplet containing one or more of the stained particles further comprises the step detecting the presence of a contaminant in the droplet.

29. The method according to claim 28 wherein the contaminant can comprises non-viable cells.

30. The method according to claim 15 wherein the step of determining the desirability of a droplet containing one or more of the stained particles further comprises the step of evaluating the extracted feature of a pulse width associated with the waveform pulse of the particle in the droplet.