Biological cell sorting and characterization using aerosol mass spectrometry

1. A computer implemented method for real-time classification of particles in a sample comprising:

acquiring a first signal from a first ion detector associated with positive ions of a particle, wherein the first signal comprises an attenuated signal and an unattentuated signal associated with the positive ions;

acquiring a second signal from a second ion detector associate with negative ions of the particle, wherein the second signal comprises an attenuated signal and an unattenuated signal associated with the negative ions;

analyzing the acquired first and second signals to detect whether enough positive and negative ions are detected to form a spectrum associated with the particle;

when enough positive and negative ions are detected to form a spectrum,

merging the attenuated and unattenuated signals associated with the positive ions to generate a first wide dynamic range measure, and

merging the attenuated and unattenuated signals associated with the negative ions to generate a second wide dynamic range measure;

computing a list of m/z peaks based on the generated first and second wide dynamic range measures; and

comparing the computed list of m/z peaks to a library of known mass spectral fingerprints to classify the particle.

2. The method of claim 1 , wherein acquiring the first and second signals comprises acquiring the signals in real-time.

3. The method of claim 1 , wherein comparing the computed list of m/z peaks with a library of known mass spectral fingerprints comprises making the comparison in real-time to classify the particle.

4. The method of claim 1 , further comprising calculating a baseline signal.

5. The method of claim 4 , wherein computing a list of peaks comprises detecting at least one of:

a peak;

a probability of starting a peak;

a peak area;

a peak bias that modifies the baseline; and

a peak width.

6. The method of claim 5 , further comprising:

subtracting the peak bias from the generated wide dynamic range measures; and

detecting whether the subtracted signal falls above or below the baseline.

7. The method of claim 1 , further comprising storing the list of m/z peaks as a vector.

8. The method of claim 7 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints to classify the particle comprises choosing an entry from the library that provides the lowest Z-score when compared with the vector, wherein calculating a Z-score comprises performing a dot product between square roots of the entry peak areas with the square roots of the vector.

9. The method of claim 8 , further comprising:

when detected that the lowest Z-score is higher than a threshold value, identifying the particle as undefined; and

when detected that the lowest Z-score is lower than or equal to the threshold value, identifying the particle as a source particle associated with the lowest Z-score.

10. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to an adaptable library that includes sources or particles from indoor or outdoor environment.

11. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to an adaptable library that includes sources or particles that includes at least one of mold, dust, pollen, bacteria, sea salt, coal, biomass burning, cars, diesel trucks, biological particles, dust, sea spray, vegetative detritus, and brake dust.

12. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to an adaptable library that includes sources or particles from at least one of ambient air or and emissions from specific sources.

13. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to the library that is adaptable to describe each known source sampled as a collection of a few typical spectra with associated variability.

14. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to the library that is adaptable to include categories of general particle types that include at least one of elemental carbon, aged organic carbon, aged elemental carbon, amines, polycyclic aromatic hydrocarbons, vanadium containing source, and ammonium containing source.

15. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to the library that is adaptable to include changes in sources due to aging.

16. The method of claim 1 , wherein comparing the computed list of m/z peaks to a library of known mass spectral fingerprints comprises comparing to the library that is adaptable to add or remove new source spectra when detected to interfere with proper classification of the particle.

17. A system for real-time classification of particles in a sample, the method comprising:

a database to store updatable library of known mass spectral fingerprints;

a data acquisition unit to acquire spectrum data associated with a particle in real-time; and

a data processing unit connected to the data acquisition unit and the database to analyze the acquired spectrum data to classify the particle in real-time, wherein, the analyzing comprises comparing the acquired spectrum data to the library of known mass spectral fingerprints stored in the database to obtain a match,

wherein the data processing unit is configured to add or remove new source spectra into the library stored in the database when detected to interfere with proper classification of the particle.

18. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to an adaptable library of known mass spectral fingerprints that includes sources or particles from indoor or outdoor environment.

19. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to an adaptable library that includes sources or particles that includes at least one of mold, dust, pollen, bacteria, sea salt, coal, biomass burning, cars, diesel trucks, biological particles, dust, sea spray, vegetative detritus, and brake dust.

20. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to an adaptable library that includes sources or particles from at least one of ambient air or and emissions from specific sources.

21. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to the library that is adaptable to describe each known source sampled as a collection of a few typical spectra with associated variability.

22. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to the library that is adaptable to include categories of general particle types that include at least one of elemental carbon, aged organic carbon, aged elemental carbon, amines, polycyclic aromatic hydrocarbons, vanadium containing source, and ammonium containing source.

23. The system of claim 17 , wherein the data processing unit is configured to compare the computed list of m/z peaks to the library that is adaptable to include changes in sources due to aging.

24. A computer implemented method for analyzing mass spectral data associated with a particle comprising:

acquiring mass spectrum data associated with a particle in real-time;

analyzing the acquired mass spectrum data to classify the particle in real-time, wherein, the analyzing comprises comparing the acquired mass spectrum data to a library of known mass spectral fingerprints to obtain a match; and

adding or removing new source spectra into the library when detected to interfere with proper classification of the particle.