Patent Publication Number: US-2022216047-A1

Title: Identification of sample subspecies based on particle mass and charge over a range of sample temperatures

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This international patent application claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/837,373, filed Apr. 23, 2019, U.S. Provisional Patent Application Ser. No. 62/839,080, filed Apr. 26, 2019, and U.S. Provisional Patent Application Ser. No. 62/950,103, filed Dec. 18, 2019, the disclosures of which are all expressly incorporated herein by reference in their entireties. 
    
    
     GOVERNMENT RIGHTS 
     This invention was made with government support under GM121751, and GM131100 awarded by the National Institutes of Health. The United States Government has certain rights in the invention. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to instruments and techniques for measuring charged sample particles, and further to such instruments and techniques for measuring charges of such particles over at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes. 
     BACKGROUND 
     Spectrometry instruments provide for the identification of chemical components of a substance by measuring one or more molecular characteristics of the substance. Some such instruments are configured to analyze the substance in solution and others are configured to analyze charged particles of the substance in a gas phase. Molecular information produced by many such charged particle measuring instruments is limited because such instruments lack the ability to measure particle charge or to process particles based on their charge. 
     SUMMARY 
     The present disclosure may comprise one or more of the features recited in the attached claims, and/or one or more of the following features and combinations thereof. In one aspect, an instrument for analyzing charged particles may comprise an ion generator configured to generate charged particles from a sample of particles, a mass spectrometer configured to receive the charged particles generated by the ion generator and to measure masses and charge magnitudes of the generated charged particles, a thermal energy source configured to transfer thermal energy to at least one of the sample particles and the charged particles generated by the ion generator, a processor, and a memory having instructions stored therein executable by the processor to cause the processor to (a) control the thermal energy source to cause the charged particles to enter the mass spectrometer at each of a plurality of different temperatures within a range of temperatures over which the sample particles undergo structural changes, (b) control the mass spectrometer to measure at least the charge magnitudes of the generated charged particles at each of the plurality of different temperatures, (c) determine an average charge magnitude of the generated charged particles at each of the plurality of different temperatures based on the measured charge magnitudes, and (d) determine an average charge magnitude profile over the range of temperatures based on the determined average charge magnitudes. 
     In another aspect, an instrument for analyzing charged particles may comprise an ion generator configured to generate charged particles from a sample of particles, a mass spectrometer configured to receive the charged particles generated by the ion generator and to measure masses and charge magnitudes of the generated charged particles, a thermal energy source configured to transfer thermal energy to at least one of the sample particles and the charged particles generated by the ion generator, a processor, and a memory having instructions stored therein executable by the processor to cause the processor to (a) control the thermal energy source to cause the charged particles to enter the mass spectrometer at each of a plurality of different temperatures within a range of temperatures over which the sample particles undergo structural changes, (b) control the mass spectrometer to measure the masses and charge magnitudes of the generated charged particles at each of the plurality of different temperatures, and (c) within a selected range of the measure masses, (i) identify all charge magnitude peaks of the measured charge magnitudes at a first one of the plurality of temperatures, and (ii) identify additional charge magnitudes of the measured charge magnitudes at each of one or more additional ones of the plurality of temperatures each having a higher temperature than that of the first one of the plurality of temperatures. 
     In yet another aspect, an instrument for analyzing charged particles may comprise an ion generator within or coupled to an ion source region, the ion generator configured to generate charged particles from a sample of particles, a mass spectrometer coupled to the ion source region, the mass spectrometer configured to receive the charged particles generated by the ion generator and to measure masses and charge magnitudes of the generated charged particles, a first pump coupled to the ion source region and configured to control an operating pressure of the ion source region, a second pump coupled to the mass spectrometer and configured to control an operating pressure of the mass spectrometer, a processor, and a memory having instructions stored therein executable by the processor to cause the processor to (a) control at least one of the first and second pumps to cause the charged particles to enter or pass through the mass spectrometer at each of a plurality of different pressures within a range of pressures over which the sample particles undergo structural changes, (b) control the mass spectrometer to measure at least the charge magnitudes of the generated charged particles at each of the plurality of different pressures, (c) determine an average charge magnitude of the generated charged particles at each of the plurality of different pressures based on the measured charge magnitudes, and (d) determine an average charge magnitude profile over the range of pressures based on the determined average charge magnitudes. 
     In still another aspect, an instrument for analyzing charged particles may comprise an ion generator within or coupled to an ion source region, the ion generator configured to generate charged particles from a sample of particles, a mass spectrometer coupled to the ion source region, the mass spectrometer configured to receive the charged particles generated by the ion generator and to measure masses and charge magnitudes of the generated charged particles, a first pump coupled to the ion source region and configured to control an operating pressure of the ion source region, a second pump coupled to the mass spectrometer and configured to control an operating pressure of the mass spectrometer, a processor, and a memory having instructions stored therein executable by the processor to cause the processor to (a) control at least one of the first and second pumps to cause the charged particles to enter or pass through the mass spectrometer at each of a plurality of different pressures within a range of pressures over which the sample particles undergo structural changes, (b) control the mass spectrometer to measure the masses and charge magnitudes of the generated charged particles at each of the plurality of different pressures, and (c) within a selected range of the measure masses, (i) identify all charge magnitude peaks of the measured charge magnitudes at a first one of the plurality of pressures, and (ii) identify additional charge magnitudes of the measured charge magnitudes at each of one or more additional ones of the plurality of pressures each having one of a higher or lower pressure than that of the first one of the plurality of pressures. 
     In a further aspect, a method for analyzing charged particles may comprise in or into an ion source region, generating charged particles from a sample of particles, causing the charged particles to enter a mass spectrometer from the ion source region at each of a plurality of differing physical and/or chemical conditions in a range of physical and/or chemical conditions in which the sample particles undergo structural changes, controlling the mass spectrometer to measure at least the charge magnitudes of the generated charged particles at each of the plurality of differing physical and/or chemical conditions, determining, with a processor, an average charge magnitude of the generated charged particles at each of the plurality of differing physical and/or chemical conditions based on the measured charge magnitudes, and determining, with the processor, an average charge magnitude profile over the range of physical and/or chemical conditions based on the determined average charge magnitudes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified diagram of an instrument for measuring and analyzing the charge magnitudes of ionized sample particles over at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes to identify and characterize new structural subspecies of the sample. 
         FIG. 2  is a simplified flowchart of an embodiment of an example process for controlling the instrument to measure sample particle mass and charge over a range of temperatures that spans the particle melting temperature(s). 
         FIG. 3A  is an example scatter plot of particle mass vs. particle charge for a sample of human HDL at 25 degrees C. generated according to the process illustrated in  FIG. 2 . 
         FIG. 3B  is another example scatter plot similar to that of  FIG. 3A  for the same sample of human HDL at 45 degrees C., also generated according to the process illustrated in  FIG. 2 . 
         FIG. 3C  is yet another example scatter plot similar to that of  FIGS. 3A and 3B  for the same sample of human HDL at 65 degrees C., also generated according to the process illustrated in  FIG. 2 . 
         FIG. 3D  is still another example scatter plot similar to that of  FIGS. 3A-3C  for the same sample of human HDL at 90 degrees C., also generated according to the process illustrated in  FIG. 2 . 
         FIG. 4  is a plot of particle mass illustrating the mass spectra of the HDL data of  FIG. 3A , along with an inset illustrating a relatively constant average mass of the sample particles over the temperature range of  FIGS. 3A-3D . 
         FIG. 5  is a simplified flowchart of an embodiment of a process for executing the final step of the process illustrated in  FIG. 2 . 
         FIG. 6  is a plot of average charge magnitude vs. temperature produced according to the process illustrated in  FIG. 5 . 
         FIG. 7  is a simplified flowchart of an embodiment of another process for executing the final step of the process illustrated in  FIG. 2 . 
         FIG. 8A  is a reproduction of the scatter plot of  FIG. 3A  partitioned into a plurality of different mass subpopulations or ranges. 
         FIG. 8B  is a plot of particle mass illustrating the contributions of the different mass subpopulations of  FIG. 8A  to the overall mass spectrum of the HDL data illustrated in  FIG. 8A . 
         FIG. 8C  is a plot of average charge magnitude vs. temperature for each of the plurality of mass subpopulations or ranges of  FIG. 8A , produced according to the process illustrated in  FIG. 7 . 
         FIG. 9  is a simplified flowchart of an embodiment of yet another process for executing the final step of the process illustrated in  FIG. 2 . 
         FIG. 10A  is a plot of abundance vs. mass-to-charge ratio of mass range number  7  of  FIGS. 8A-8C  at a number of different temperatures, produced according to the process illustrated in  FIG. 9 . 
         FIG. 10B  is a plot of charge abundance vs temperature illustrating charge abundance profiles of the subspecies illustrated in  FIG. 10A , produced according to the process illustrated in  FIG. 9 . 
     
    
    
     DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS 
     For the purposes of promoting an understanding of the principles of this disclosure, reference will now be made to a number of illustrative embodiments shown in the attached drawings and specific language will be used to describe the same. 
     This disclosure relates to apparatuses and techniques for measuring particle charges of a sample over at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes, and for analyzing the resulting measurements to identify new structural subspecies as a function of at least particle charge. For purposes of this document, the terms “charged particle” and “ion” may be used interchangeably, and both terms are intended to refer to any particle having a net positive or negative charge. The term “charge magnitude” should be understood to mean the number of charges, i.e., the number of elemental charges “e,” of a charged particle, such that the terms “charge magnitude” and “number of charges of a charged particle” are synonymous and may be used interchangeably. A charged particle having a charge of 50 e thus has a charge magnitude of 50 e. 
     The phrase “at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes” should be understood to mean any set or progression of changing physical conditions to which the sample particles are subjected before and/or after ionization thereof in or during which the sample particles undergo structural changes, any set or progression of changing chemical conditions to which the sample particles are subjected before and/or after ionization thereof in or during which the sample particles undergo structural changes, and/or any combination of one or more such sets or progressions of changing physical and/or chemical conditions in or during which the sample particles undergo structural changes. An example of such physical conditions may include, but is not limited to, sample and/or charged particle temperature, such that a range of differing physical conditions is defined by a range of differing or changing temperatures to which the sample and/or charged particles are subjected. Another example of such physical conditions may include, but is not limited to, sample and/or charged particle pressure, such that a range of differing physical conditions is defined by a range of differing or changing pressures to which the sample and/or charged particles are subjected, or the like. An example of such chemical conditions may include, but is not limited to, a sample in the form of a mixture or solution in which the content or makeup of the mixture or solution changes, such that a range of differing or changing chemical conditions of the sample mixture or solution is defined by changes in the content or makeup of the sample mixture or solution, e.g., by adding and/or removing components to/from the sample mixture or solution, by changing the relative concentrations in the sample mixture or solution of two or more of its components, etc. Another example of such chemical conditions may include, but is not limited to, a chemical reaction between two or more components of a mixture or solution following combining such components together into, or to form, the mixture or solution, such that a range of differing or changing chemical conditions of the sample mixture or solution is defined by changes in the chemical properties of a newly formed mixture or solution as the components chemically react with one another over some period of time, e.g., up to and including an equilibrium of the mixture or solution. It is to be understood that the phrase “at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes” may be or include a single range of a differing physical condition, a single range of a differing chemical condition, two or more ranges of the same or different changing physical conditions, two or more ranges of the same or different changing chemical conditions, or any combination of the foregoing. In any case, the term “structural changes” should be understood to mean any detectable, i.e., measurable, change in the structure(s) of one or more of the sample particles. Examples of such structural changes that a sample particle may undergo may include, but are not limited to, any conformational change, dissociation of a dimer, tetramer or larger macromolecular assembly into fragments, loss of a small ligand (e.g., drug), and/or any change that results in aggregation, assembly or related phenomena. It will be further understood that the term “melting transition” will refer to a structural change that a particle undergoes at a corresponding “melting temperature” thereof, and that the term “melting profile” will refer to the behavior of one or more properties of a particle within a specified temperature range which includes, i.e., which passes through, a melting temperature thereof. 
     Referring now to  FIG. 1 , a diagram is shown of an instrument  10  for measuring and analyzing mass and charge of ionized sample particles over a at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes to identify new structural subspecies of the sample. In the illustrated embodiment, the instrument  10  illustratively includes an ion source region  12  having an outlet coupled to an inlet of a mass spectrometer  14 . The ion source region  12  illustratively includes an ion generator  16  configured to generate ions, i.e., charged particles, from a sample  18 . The ion generator  16  is illustratively implemented in the form of any conventional device or apparatus for generating ions from a sample. As one illustrative example, which should not be considered to be limiting in any way, the ion generator  16  may be or include a conventional electrospray ionization (ESI) source, a matrix-assisted laser desorption ionization (MALDI) source or other conventional ion generator configured to generate ions from the sample  18 . The sample from which the ions are generated may be any biological or other material, or any mixture of biological and/or non-biological components. In some embodiments, the sample  18  may be dissolved, dispersed or otherwise carried in solution, although in other embodiments the sample may not be in or part of a solution. 
     In the illustrated embodiment, a voltage source VS 1  is electrically connected to a processor  20  via a number, J, of signal paths, where J may be any positive integer, and is further electrically connected to the ion source region  12  via a number, K, of signal paths, where K may likewise be any positive integer. In some embodiments, the voltage source VS 1  may be implemented in the form of a single voltage source, and in other embodiments the voltage source VS 1  may include any number of separate voltage sources. In some embodiments, the voltage source VS 1  may be configured or controlled to produce and supply one or more time-invariant (i.e., DC) voltages of selectable magnitude. Alternatively or additionally, the voltage source VS 1  may be configured or controlled to produce and supply one or more switchable time-invariant voltages, i.e., one or more switchable DC voltages. Alternatively or additionally, the voltage source VS 1  may be configured or controllable to produce and supply one or more time-varying signals of selectable shape, duty cycle, peak magnitude and/or frequency. 
     The processor  20  is illustratively conventional and may include a single processing circuit or multiple processing circuits. The processor  20  illustratively includes or is coupled to a memory  22  having instructions stored therein which, when executed by the processor  20 , cause the processor  20  to control the voltage source VS 1  to produce one or more output voltages for selectively controlling operation of the ion generator  16 . In some embodiments, the processor  20  may be implemented in the form of one or more conventional microprocessors or controllers, and in such embodiments the memory  22  may be implemented in the form of one or more conventional memory units having stored therein the instructions in a form of one or more microprocessor-executable instructions or instruction sets. In other embodiments, the processor  20  may be alternatively or additionally implemented in the form of a field programmable gate array (FPGA) or similar circuitry, and in such embodiments the memory  22  may be implemented in the form of programmable logic blocks contained in and/or outside of the FPGA within which the instructions may be programmed and stored. In still other embodiments, the processor  20  and/or memory  22  may be implemented in the form of one or more application specific integrated circuits (ASICs). Those skilled in the art will recognize other forms in which the processor  20  and/or the memory  22  may be implemented, and it will be understood that any such other forms of implementation are contemplated by, and are intended to fall within, this disclosure. In some alternative embodiments, the voltage source VS 1  may itself be programmable to selectively produce one or more constant and/or time-varying output voltages. 
     In the illustrated embodiment, the voltage source VS 1  is illustratively configured to be responsive to control signals produced by the processor  20  to produce one or more voltages to cause the ion generator  16  to generate ions from the sample  18 . In some embodiments, the sample  18  is positioned within the ion source region  12 , as illustrated in  FIG. 1 , and in other embodiments the ion source  18  is positioned outside of the ion source region  12 . In one example embodiment, which should not be considered to be limiting any way, the sample  18  is provided in the form of a solution and the ion generator  16  is a conventional electrospray ionization (ESI) source configured to be responsive to one or more voltages supplied by VS 1  to generate ions from the sample  18  in the form of a fine mist of charged droplets. It will be understood that ESI and MALDI, as described hereinabove, represent only two examples of myriad conventional ion generators, and that the ion generator  16  may be or include any such conventional device or apparatus for generating ions from a sample whether or not in solution. 
     In the illustrated embodiment, the instrument  10  includes a thermal energy source  24  is configured to selectively thermally energize, i.e., transfer thermal energy to, the sample  18  and/or to the charged particles exiting the ion generator  16  prior to entrance of the charged particles into the mass spectrometer  14 . In some embodiments, examples of which will be described below, the thermal energy source  24  may not be utilized, and in such embodiments the thermal energy source  24  may be omitted. In some embodiments, the thermal energy may be in the form of heat transferred from the source  24  to the sample particles, and in other embodiments the thermal energy may be in the form of heat transferred from the sample particles to the source  24 , i.e., cooling of the sample particles. In some embodiments, the source  24  may include both heating and cooling capabilities so that the sample temperature may be swept through ambient temperature from warmer to cooler or from cooler to warmer, or may be swept from any of cold to colder, colder to less cold, cold or cool to warm or hot, warm or hot to cool or cold, warm to warmer, warmer to less warm, warm to hot, hot to warm, etc. Example heat sources  24  may include, but are not limited to, conventional solution heaters and heating units, one or more sources of radiation, e.g., infrared, laser, microwave or other, at any radiation frequency, one or more heated gasses or other fluid(s) or the like, and example cooling sources  24  may include, but are not limited to, conventional solution chillers, one or more chilled gasses or other fluid(s), or the like. 
     In some embodiments, as illustrated by example in  FIG. 1 , the thermal energy source  24  is electrically connected to the voltage source VS 1 , and the voltage source VS 1  is configured to be responsive to one or more control signals produced by the processor  20  to produce one or more corresponding voltages to control thermal energy produced by the thermal energy source  24 . In alternate embodiments, the thermal energy source  24  may be configured to be responsive to control signals produced by the processor  20  to selectively produce thermal energy, and in such embodiments the thermal energy source  24  may be electrically connected directly, or via conventional circuitry, to the processor  20  as illustrated by dashed-line representation in  FIG. 1 . In any case, in one embodiment the thermal energy source  24  may be implemented in the form of one or more conventional heaters or heating elements and/or one or more conventional coolers or cooling elements, coupled to the sample  18 , e.g., in the form of a solution, mixture or otherwise. In this embodiment, the thermal energy source  24  is responsive to one or more voltages produced by the voltage source VS 1  and/or to one or more control signals produced by the processor  20 , to control the temperature of the sample  18  of uncharged particles to a target temperature by heating or cooling the sample  18  to the target temperature. Charged particles generated by the ion generator  16  from the sample  18  thus enter the mass spectrometer  14  at the target temperature. 
     Alternatively or additionally, the thermal energy source  24  may be implemented in the form of one or more devices for thermally energizing charged particles exiting the ion generator  16  and prior to entrance into the mass spectrometer  14 . In this embodiment, the thermal energy source  24  is responsive to one or more voltages produced by the voltage source VS 1  and/or to one or more control signals produced by the processor  20 , to control the temperature of the charged particles exiting the ion generator  16  to a target temperature by heating or cooling the charged particles prior to entry into the mass spectrometer  14 . As with the sample temperature control embodiment, the charged particles generated by the ion generator  16  likewise enter the mass spectrometer  14  at the target temperature. In any case, it will be understood that the target temperature may be any temperature above or below ambient. Some examples of such a thermal energy source  24  and operation thereof for heating the ionized particles are disclosed in co-pending International Application No. PCT/US2018/064005, filed Dec. 5, 2018, the disclosure of which is incorporated herein by reference in its entirety. Those skilled in the art will recognize other structures and/or techniques for controlling the temperature of charged particles entering the mass spectrometer  14 , by heating or cooling prior to or after inducing charge thereon, and it will be understood that any such other structures and/or techniques are intended to fall within the scope of this disclosure. 
     In some embodiments, one or more conventional sensors  25  may optionally be operatively coupled to the ion source region  12  and electrically coupled to the processor  20  as illustrated in  FIG. 1  by dashed line representation. In such embodiments, the one or more sensors  25  is/are illustratively configured to provide one or more sensor signals to the processor  20  corresponding to the operating temperature of the thermal energy source  24 , the temperature of the sample  18  and/or the temperature of the charged particles exiting the ion generator  16  and entering the mass spectrometer  14 , or to provide one or more sensor signals to the processor  20  from which the operating temperature of the thermal energy source  24 , the temperature of the sample  18  and/or the temperature of the charged particles exiting the ion generator  16  and entering the mass spectrometer  14  can be determined or estimated. 
     The mass spectrometer  14  illustratively includes two sections coupled together; an ion processing region  26  and an ion detection region  28 . A second voltage source VS 2  is electrically connected to the processor  20  via a number, L, of signal paths, where L may be any positive integer, and is further electrically connected to the ion processing region  26  via a number, M, of signal paths, where M may likewise be any positive integer. In some embodiments, the voltage source VS 2  may be implemented in the form of a single voltage source, and in other embodiments the voltage source VS 2  may include any number of separate voltage sources. In some embodiments, the voltage source VS 2  may be configured or controlled to produce and supply one or more time-invariant (i.e., DC) voltages of selectable magnitude. Alternatively or additionally, the voltage source VS 2  may be configured or controlled to produce and supply one or more switchable time-invariant voltages, i.e., one or more switchable DC voltages. Alternatively or additionally, the voltage source VS 2  may be configured or controllable to produce and supply one or more time-varying signals of selectable shape, duty cycle, peak magnitude and/or frequency. As one specific example of the latter embodiment, which should not be considered to be limiting in any way, the voltage source VS 2  may be configured or controllable to produce and supply one or more time-varying voltages in the form of one or more sinusoidal (or other shaped) voltages in the radio frequency (RF) range. 
     In some embodiments, the mass spectrometer  14  is configured to measure both mass and charge magnitudes of charged particles generated by the ion generator  16  as illustrated by example in  FIG. 1 . In such embodiments, the ion detection region is electrically connected to input(s) of each of a number, N, of charge detection amplifiers CA, where N may be any positive integer, and output(s) of the number, N, of charge detection amplifiers CA is/are electrically connected to the processor  20  as shown in  FIG. 1 . The charge amplifier(s) CA is/are each illustratively conventional and responsive to charges induced by charged particles on one or more respective charge detectors disposed in the charge detection region  28  to produce corresponding charge detection signals at the output thereof, and to supply the charge detection signals to the processor  20 . 
     In one embodiment in which the mass spectrometer  14  is provided in the form of a mass spectrometer configured to measure both mass and charge magnitudes of charged particles generated by the ion generator  16 , the mass spectrometer  14  may be implemented in the form of a charge detection mass spectrometer (CDMS), wherein the ion processing region  26  is or includes a conventional mass spectrometer or mass analyzer and the ion detection region  28  illustratively includes one or more corresponding CDMS charge detectors. In some embodiments, the one or more CDMS charge detectors may be provided in the form of one or more electrostatic linear ion traps (ELITs), and in other embodiments the one or more CDMS charge detectors may be provided in the form of at least one orbitrap. In some embodiments, the CDMS charge detector(s) may include at least one ELIT and at least one orbitrap. CDMS is illustratively a single-particle technique typically operable to measure mass and charge magnitude values of single ions, although some CDMS detectors have been designed and/or operated to measure mass and charge of more than one charged particle at a time. Some examples of CDMS instruments and/or techniques, and of CDMS charge detectors and/or techniques, which may be implemented in the mass spectrometer  14  of  FIG. 1  are disclosed in co-pending International Application Nos. PCT/US2019/013251, PCT/US2019/013274, PCT/US2019/013277, PCT/US2019/013278, PCT/US2019/013280, PCT/US2019/013283, PCT/US2019/013284 and PCT/US2019/013285, all filed Jan. 11, 2019, and the disclosures of which are all incorporated herein by reference in their entireties. 
     In another embodiment in which the mass spectrometer is provided in the form of a mass spectrometer configured to measure both mass and charge magnitudes of charged particles generated by the ion generator  16 , the mass spectrometer  14  may be implemented in the form of a mass spectrometer configured to measure mass-to-charge ratios of charged particles and further configured to simultaneously measure charge magnitudes of the charged particles. In such embodiments, the ion processing region  26  is or includes an ion acceleration region and/or a scanning mass-to-charge ratio filter, and the ion detection region  28  illustratively includes a charge detector array disposed in an electric field-free drift region or drift tube. In such embodiments, a conventional ion detector  30 , e.g., a conventional microchannel plate detector or other conventional ion detector, is positioned at the outlet end of the drift region or drift tube and is electrically connected to the processor as illustrated by dashed-line representation in  FIG. 1 . Some example embodiments of such a mass spectrometer are disclosed in U.S. Patent Application 62,949/554, filed Dec. 18, 2019 and entitled MASS SPECTROMETER WITH CHARGE MEASUREMENT ARRANGEMENT, the disclosure of which is incorporated herein by reference in its entirety. 
     Regardless of the particular form in which the mass spectrometer  14  is provided, the various sections of the instrument  10  are controlled to sub-atmospheric pressure for operation thereof as is conventional. In the illustrated embodiment, for example, a so-called vacuum pump P 1  is operatively coupled to the ion source region  12 , another vacuum pump P 2  is operatively coupled to the ion processing region  26  of the mass spectrometer  14  and yet another vacuum pump P 2  is operatively coupled to the ion detection region  28  of the mass spectrometer. In the illustrated embodiment, each of the pumps P 1 , P 2  and P 3  is electrically coupled to the processor  20  such that the processor  20  is configured to control operation of each of the pumps P 1 , P 2  and P 3  and therefore independently control the pressures in each of the three respective regions  12 ,  26  and  28 . In alternate embodiments, one or more of the pumps P 1 , P 2  and/or P 3  may be manually controlled. In still other embodiments, more or fewer pumps may be implemented to control the pressure in more or fewer respective portions of the instrument  10 . In some embodiments in which the thermal energy source  24  is omitted, the sensor  25  may be provided in the form of a pressure sensor operable to provide a pressure signal to the processor  20  from which the processor  20  is operable to determine or estimate the pressure within the ion source region  12 . In embodiments in which the thermal energy source  24  is included, the sensor  25  may include a temperature sensor and a pressure sensor. In any case, one or more additional pressure sensors may be operatively coupled to the ion processing region  26  and/or to the ion detection region  28  for determination by the processor  20  of the pressure(s) in this/these region(s). 
     In other embodiments, one or more examples of which will be described further below, the mass spectrometer  14  may be provided in the form of any conventional mass spectrometer configured to measure mass-to-charge ratios of charged particles generated by the ion generator  16 . In such embodiments, the ion processing region  26  may typically be implemented in the form of a conventional ion acceleration region, the ion detection region  28  will be implemented in the form of one or more conventional drift tubes, the charge amplifier(s) CA will be omitted and the ion detector  30  or other ion detector suitably positioned in the mass spectrometer will be included. 
     Referring now to  FIG. 2 , a simplified flowchart is shown depicting an example process  50  for operating the mass spectrometer  10  of  FIG. 1  to measure charge and mass of charged particles generated from a sample over a range of temperatures, and for analyzing the resulting measurements to identify new structural subspecies as a function of particle charge and/or particle mass and/or particle mass to charge-ratio. In the illustrated process  50 , the range of temperatures illustratively spans the melting temperature(s) of the particles generated from the sample  18  at which the sample particles undergo respective “melting transitions” as this term is defined above. The process  50  is illustratively stored in the memory  22  in the form of instructions executable by the processor  20  to carry out the measurements and analysis. The process  50  illustratively begins at step  52  where the processor  20  is illustratively operable to set a counter i equal to 1 or to some other constant. Thereafter at step  54 , the processor  20  is operable to control the voltage source VS 1  to produce one or more voltages, and/or to control the thermal energy source  24  directly, to control the ion generator  16  and the thermal energy source  24  to cause the charged particles generated by the ion generator  16  to enter the mass spectrometer  14  at a target temperature T(i). In embodiments in which the thermal energy source  24  is coupled to the sample  18 , e.g., in solution or otherwise, step  54  of the process  50  illustratively includes steps  56 ,  58  and  60  as illustrated by example in  FIG. 2 . In this embodiment of the process  50 , the processor  20  is operable at step  56  to cause the thermal energy source  24  to control the temperature of the sample  18  to a target temperature T(i). Thereafter, the processor  20  is illustratively operable at step  58  to monitor the one or more sensors  25 , in embodiments which include the one or more sensors  25 , and to determine from sensor signals produced thereby, in a conventional manner, whether the operating temperature of the sample  18  has stabilized at T(i). If so, then the process  50  advances to step  60 , and otherwise the process  50  loops back to step  56 . In embodiments which do not include the one or more sensors  25 , step  58  may illustratively be or include a selectable time delay to allow the temperature of the sample  18  to increase/decrease following execution of step  56 , and in such embodiments the process  50  advances from step  58  to step  60  only after expiration of the selectable time delay. In any case, at step  60  the processor  20  is illustratively operable to control the voltage source VS 1  to produce one or more voltages to control the ion generator  16  to generate charged particles from the sample  18  at the target temperature T(i). Charged particles generated from the sample  18  by the ion generator  16  thus enter the mass spectrometer  14  at the temperature T(i). 
     In other embodiments in which the thermal energy source  24  is configured and positioned relative to the ion source region  12  to operate on the charged particles exiting the ion generator  16 , step  54  of the process  50  illustratively includes step  60  followed by step  56 . The processor  20  is operable at step  60  to control the voltage source VS 1  to produce one or more voltages to cause the ion generator  16  to generate charged particles, and is then operable at step  56  to control the voltage source VS 1  to produce one or more voltages, and/or to control the thermal energy source  24  directly, to cause the thermal energy source  24  to control the temperature of the charged particles exiting the ion generator  16  and entering the mass spectrometer  14  to the temperature T(i). In embodiments which include the one or more sensors  25 , the processor  20  may be further operable at step  56  to control the voltage source VS 1  and/or the thermal energy source  24  based on feedback signal(s) produced by the one or more sensors  25 . In any case, charged particles generated from the sample  18  by the ion generator  16  enter the mass spectrometer  14  at the target temperature T(i). 
     Following step  54 , the processor  20  is illustratively operable at step  62  to control the voltage source VS 2  to supply the charged particles at the target temperature T(i) exiting the ion source region  12  and entering the ion processing region  26  of the mass spectrometer  14  to the charge detection region  28  of the mass spectrometer  14 . Based on the signals produced by the one or more charge amplifiers CA, and in some embodiments on signals produced by the ion detector  30  as described above, the processor  20  is operable thereafter at steps  64 - 68  to determine mass and charge magnitude values of the charged particles at the target temperature T(i), and to store the particle mass and charge magnitude measurements at T(i) in the memory  22 . In embodiments in which the mass spectrometer  14  is a CDMS, steps  62 - 68  are illustratively repeated until all, or at least a desired subset, of the different charged particles generated from the sample  18  are processed. 
     Following step  68 , the process  50  advances to step  70  where the processor  20  is operable to determine whether the current count value i has advanced to an end count value S. If not, the process  50  advances to step  72  where the count value i is incremented by 1 and the process  50  then loops back to step  54  to re-execute the process  50  at another temperature. The temperature range over which the process  50  is executed may be any temperature range in which the particles generated from the sample  18  undergo structural changes. In one example implementation of the process  50 , the temperature range over which the process  50  is executed is a temperature range which spans the melting temperatures of the particles generated from the sample  18 , and the total number of incremental temperatures within the selected temperature range over which the process  50  is executed may be any integer number such that the step size between incremental temperatures may be any desired step size. It will be understood that the temperature range may illustratively be advanced in the process  50  from the coolest temperature to the warmest, or vice versa, or the temperature may instead be controlled non-linearly. 
     As one example, which should not be considered to be limiting in any way, the temperature range over which the process  50  is executed may be 65 degrees C., which may illustratively begin at 25 degrees C. and end at 90 degrees C., with a step size of 5 degrees C. between each execution of the process  50  so that mass and charge values of the charged particles generated from the sample  18  are measured at 25 degrees C., 30 degrees C., 35 degrees C., . . . , 85 degrees C. and 90 degrees C. It will be understood that in other embodiments, the temperature range may be greater or lesser than 65 degrees C., the coolest temperature may be greater or lesser than 25 degrees C., the warmest temperature may be greater or lesser than 90 degrees C. and/or the steps size between temperatures may be greater or less than 5 degrees C. 
     Referring to  FIGS. 3A-3D , four examples of steps  52 - 72  of the process  50  are shown in the form of scatter plots of particle charge magnitude (in units of elementary charge e) vs. particle mass (in units of mega-daltons MDa) of a sample  18  of HDL (high density lipoproteins) from which charged particles were generated by an ESI source and measured by a mass spectrometer  14  implemented in the form of a single-particle processing CDMS instrument. In these examples, the thermal energy source  24  was implemented in the form of a conventional heating device coupled to the sample  18  in solution. In  FIG. 3A , the scatter plot was generated from charged particles measured at 25 degrees C., and the scatter plots of  FIGS. 3B, 3C and 3D  were generated from charged particles measured at 45 degrees C., 65 degrees C. and 90 degrees C. respectively. It will be understood that while the particles illustrated in  FIGS. 3A-3D  have masses in the MDa range, nothing in this disclosure should be understood as limiting the sample  18  to mixtures, solutions or substances made up of particles only in this mass range. Rather, it should be understood that the concepts described herein are applicable to mixtures, solutions and substances made up of particles in any mass range. Likewise, it should be understood that the sample  18  is not limited to the example HDL sample but may instead be a sample of any material, in any form, without limitation. 
     From the plots illustrated in  FIGS. 3A-3D , the data appears to disperse with increasing temperature. However, as illustrated in  FIG. 4 , the average mass of the sample  18  of HDL does not appear to deviate significantly from the average mass value of 324 kDa over the temperature range 25 degrees C.-90 degrees C. As such, the dispersion of the data illustrated in  FIGS. 3A-3D  is attributable to temperature-dependent changes in the charge magnitudes of the charged particles generated from the sample  18 . In this regard, the process  50  of  FIG. 2  advances from the YES branch of step  70  to step  74  where the processor  20  is operable to process the particle mass and charge measurements taken at the various different temperatures T(1)-T(S) to determine particle charge-related information. 
     Referring now to  FIG. 5 , a simplified flowchart is shown of an embodiment of a process  74 A for executing step  74  of the process  50  illustrated in  FIG. 2 . The process  74 A is illustratively stored in the memory  22  in the form of instructions executable by the processor  20  to carry out processing of the particle mass and charge measurements taken at the various different temperatures T(1)-T(S) to determine particle charge-related information in the form of a charge melting profile of the sample  18  over the temperature range T(1)-T(S). The process  74 A begins at step  80  where the processor  20  is operable to compute an average particle charge magnitude CH AV  for each temperature in the temperature range T(1)-T(S) at which charged particles were generated and measured by the instrument  10  in the process  50  of  FIG. 2 . In one embodiment, the processor  20  is operable at step  80  to compute the average particle charge magnitude CH AV  at each such temperature as an algebraic average of the measured charge magnitudes. In other embodiments, the processor  20  may be operable to compute such averages using one or more alternate averaging techniques. Keeping with the example described above with respect to  FIGS. 3A-3D , the processor  20  is illustratively operable in this example at step  80  to compute CH AV  for each temperature in increments of 5 degrees C. between 25 degrees C. and 90 degrees C. 
     Following step  80 , the processor  20  is operable at step  82  to compute an average charge magnitude melting profile over the temperature range T(1)-T(S) based on the average charge magnitudes CH AV  computed at step  80  for each temperature in the temperature range T(1)-T(S). Thereafter at step  84 , the processor  20  is operable to store the average charge magnitude melting profile computed at step  82  and, in some embodiment, to display the same. Again referring to the example described above with respect to  FIGS. 3A-3D , an average charge melting profile thereof is illustrated by example in  FIG. 6 . As evident from  FIG. 6 , the particle charge magnitudes of the HDL sample  18  exhibit a relatively constant average charge value of around 35 e for temperatures below about 60 degrees C., and then undergo a melting transition centered at about 66 degrees C., and at temperatures above about 75 degrees C. the particle charge magnitudes of the HDL sample  18  exhibit a relatively constant average charge value of around 42 e. 
     Referring now to  FIG. 7 , a simplified flowchart is shown of an embodiment of another process  74 B for executing step  74  of the process  50  illustrated in  FIG. 2 . The process  74 B is illustratively stored in the memory  22  in the form of instructions executable by the processor  20  to carry out processing of the particle mass and charge measurements taken at the various different temperatures T(1)-T(S) to determine particle charge-related information in the form of charge melting profiles for subpopulations of particles in each of multiple different mass ranges of the sample  18  over the temperature range T(1)-T(S). Referring to  FIG. 8A , for example, the plot of  FIG. 4A  is reproduced upon which several vertical dashed lines are superimposed illustrating partitioning of the charge magnitude vs. mass measurements into seven different, side-by-side mass ranges. In  FIG. 8B , a mass abundance spectrum is shown of the partitioned mass ranges depicting the average mass values of the particles in each mass range. In the illustrated example, the average mass value of the particles in mass range  1  is 120 kDa, the average mass value of the particles in mass range  2  is 170 kDa, and the average mass values of the particles in mass ranges  3  through  7  are 214, 270, 346, 440 and 618 kDa respectively. According to the process  74 B illustrated in  FIG. 7 , the processor  20  is operable to process the particle mass and charge measurements taken at the various different temperatures T(1)-T(S) to determine charge melting profiles the subpopulations of particles in each of the multiple different mass ranges of the sample  18  over the temperature range T(1)-T(S). The process  74 B begins at step  100  where the processor  20  is operable to set a counter j equal to 1 or to some other constant. Thereafter at step  102 , the processor  20  is operable to compute an average particle charge magnitude CH AV , using any conventional averaging technique, for each of the particles within the mass range MR(j) of the charged particles in each temperature range T(1)-T(S) at which charged particles were generated and measured by the instrument  10  in the process  50  of  FIG. 2 . Thereafter at step  104 , the processor  20  is operable to compute an average charge magnitude melting profile for the mass range MR(j) based on the average charge magnitudes CH AV  computed at step  102  for each temperature in the temperature range T(1)-T(S). Thereafter at step  106 , the processor  20  is operable to determine whether the count value j has reached a count value Z equal to the total number of partitioned mass ranges. If not, the process  74 B advances to step  108  where the processor  20  increments the counter j before looping back to step  102 . If, at step  106 , j=Z, the process  74 B advances to step  110  where the processor  20  is operable to store the average charge magnitude melting profiles computed at step  104  and, in some embodiment, to display the same. Referring to the example described above with respect to  FIGS. 8A and 8B , average charge melting profiles of the charged particles in each of the seven mass ranges are illustrated by example in  FIG. 8C . Each mass range has a separate and distinct average charge melting profile, and each has a different average melting temperature; e.g., 59 degrees C. for mass range  1 , 62 degrees C. for mass range  2 , etc. 
     Referring now to  FIG. 9 , a simplified flowchart is shown of an embodiment of yet another process  74 C for executing step  74  of the process  50  illustrated in  FIG. 2 . The process  74 C is illustratively stored in the memory  22  in the form of instructions executable by the processor  20  to carry out processing of the particle mass and charge measurements taken at the various different temperatures T(1)-T(S) to determine particle charge-related information in the form of newly observed families of structures for subpopulations of particles in different mass ranges of the sample  18  over the temperature range T(1-T(S). In accordance with the process  74 C, the particle mass and charge measurements taken at the various different temperatures T(1-T(S) are processed within each mass range subpopulation as a function of temperature to identify additional subspecies, if any, via detectable peaks or groupings. The process  74 C begins at step  150  where the processor  20  is operable to set a counter k equal to one or some other constant. Thereafter at step  152 , the processor  20  is operable to analyze the charge magnitude measurements in a selected mass range at one of the temperatures T(k) at which the charged particles were measured by the instrument  10  to identify any new subspecies, if any, via detectable peaks or groupings. At step  154 , the processor  20  is operable to store any subspecies peaks or groupings identified at the temperature T(k). Thereafter at step  156 , the processor  20  is operable to determine whether the current value of the counter k is equal to a temperature count value Y. If not, the process  74 C advances to step  158  where the processor  20  increments the value of k before looping back to step  152 , and otherwise the process  74 C advances to step  160 . 
     At step  160 , the processor  20  is illustratively operable to display the identified subspecies peaks/groupings for one or more of the temperatures T k -T Y . Thereafter at step  162 , the processor  20  is illustratively operable to compute charge magnitude abundance profiles for each such subspecies peak/grouping over the temperature range T k -T Y . Thereafter at step  164 , the processor  20  is illustratively operable to store the results of the previous steps and, in some embodiments, to display the charge magnitude abundance profiles. 
     In some embodiments, the processor  20  may be operable to execute step  152  by analyzing only the charge magnitude measurements within the selected mass range subpopulation, although in other embodiments it may be useful to analyze abundance peaks of the measurements converted to mass-to-charge ratio values. The latter case is illustrated by an example execution of step  160  of the process  74 C in  FIG. 10A  which depicts abundance vs. mass-to-charge ratio plots of the subpopulation of the charged particles in mass range  7  of  FIGS. 8A-8C  as a function of temperature. As the temperature of the subpopulation of charged particles in mass range  7  increases, well-defined, high charge state subspecies emerge in the mass-to-charge ratio spectrum. At 25 degrees C., for example, a single z=45 e peak is observed at a mass-to-charge ratio (m/z) of approximately 13 kTh. As the temperature is increased to 55 degrees C., the fraction of 13 kTh particles decreases which results in a shift of the m/z peak to approximately 12.5 kTh and a new subspecies is observed with a z=56 e peak. As the masses of these particles have not changed, as described above with respect to  FIG. 4 , the newly observed subspecies correspond to changes in the average charge of the particles. As the temperature is further increased to 65° C. the z=56 e subspecies increases in abundance and additional subspecies emerges with z=73 e, z=81 e and Z=106 e respectively. At another increased solution temperature of 75° C. yet another subspecies emerges with z=123. In total the z=45 e precursor gives rise to at least five new resolvable subspecies. 
     An example of steps  162  and  164  of the process  74 C is illustrated in  FIG. 10B  which depicts a plot of the charge magnitude abundance profiles of the subspecies illustrated in  FIG. 10A  as a function of temperature. The top curve in  FIG. 10B  is the precursor charge state, and the bottom five curves in  FIG. 10B  correspond to the five new subspecies identified at steps  152 - 158  and illustrated by example in  FIG. 10A . The plot of  FIG. 10B  reveals that each subspecies observed in  FIG. 10A  has a unique formation temperature, and that approximately 45% of subpopulation  7 , i.e., mass range  7 , is a subspecies that does not appear to melt, even at the highest temperature of approximately 90 degrees C. The remaining subpopulations behave similarly—providing evidence for as few as three, to as many as six subspecies, within each subpopulation. Each subspecies is delineated based on its charge and unique formation temperature. In total, the  7  subpopulations, i.e., 7 mass ranges illustrated in  FIGS. 8A and 8B , evolve into 28 unique subspecies. In every case, subspecies that are discernable at elevated temperatures disappear upon cooling the solution, regenerating the seven initial subpopulations. That is, each transition is reversible, although in some instances not all transitions may be reversible. The new high temperature subspecies arise when distinct subspecies that are present, but unresolved and therefore hidden at low temperatures, undergo unique melting transitions with increasing temperatures that enable them to be resolved. 
     Average charge magnitude melting profiles of the types illustrated in  FIGS. 6 and 8C  for an HDL sample  18 , as well as the emergence of additional high charge-state subspecies within mass-range subpopulations of particles as illustrated in  FIGS. 10A and 10B  for the same HDL sample  18 , provide a useful measure of the stability of a sample over temperature. Temperature stability of particles is particularly useful in the investigation of biological substances, an example of which includes, but is not limited to, viruses, and particularly those used for gene therapy products. The temperature stabilities of gene therapy products may be related to the efficacy of such products, i.e., in terms of explaining why some gene therapy products are therapeutically active and others are not. Moreover, it will be understood that while the sample  18  used in the examples illustrated in  FIGS. 3A-3D, 4, 6, 8A-8C and 10A-10B  is a high density lipoprotein (HDL) sample, in other applications the sample  18  may be any material whether or not biological in nature and whether in solution or otherwise. Additional example biological substances or materials that may be used as the sample  18  may include, but are not limited to, exomes, endosomes, microvessicles generally, ectosomes, apoptotic bodies, gene therapies, retroviruses, exomeres, chylomicrons, DNA, RNA, proteins, fats, acids, carbohydrates, enzymes, viruses, bacteria, or the like. 
     As described at the outset, this disclosure relates to apparatuses and techniques for measuring particle charges of a sample over at least one range of differing physical and/or chemical conditions in which the sample particles undergo structural changes, and for analyzing the resulting measurements to identify new structural subspecies as a function of at least particle charge. In this regard, the processes illustrated in  FIGS. 2, 5, 7 and 9 , as well as the data illustrated in  FIGS. 3A-3D, 4, 6, 8A-8C and 10A-10B , represent one example embodiment in which particle charges are measured over a range of changing temperatures, which illustratively span melting temperatures of the particles, via control of the thermal energy source  24  as depicted in  FIGS. 2-4 , and in which the measured charge data is thereafter analyzed according to the processes illustrated in  FIGS. 5, 7 and 9  to produce the information illustrated in  FIGS. 6, 8A-8C and 10A-10B . 
     In one alternate embodiment, the particle charges may be instead be measured over a range of changing instrument pressures via control of one or more of the pumps P 1 , P 2 , P 3  depicted in  FIG. 1 . In this embodiment, step  56  of the process  50  illustrated in  FIG. 2  will be modified to control P 1 , P 2  and/or P 3  to a target pressure P(i), and the pressure value(s) will then be incrementally changed at steps  70  and  72  until the sample particles have been subjected to a range of different pressure conditions in which the sample particles undergo structural changes. The process  74 A illustrated in  FIG. 5  will then be modified to compute an average particle charge magnitude for each pressure value, and to compute a charge magnitude pressure profile based on the average particle charge magnitude values over the pressure range. The processes  74 B and  74 C illustrated in  FIGS. 7 and 9  respectively will likewise be modified to process the charge magnitude values at the various pressure values and in the various mass ranges. 
     In another alternate embodiment, the particle charges may be instead be measured over a range of changing sample compositions (i.e. changing sample content or makeup), with each one or more sample composition changes being carried out by adding one or more components to the sample  18 , removing one or more components from the sample  18 , changing the relative concentration of one or more components relative to one or more other components, or the like. In this embodiment, step  56  of the process  50  illustrated in  FIG. 2  will be modified to carry out a change in the composition of the sample  18 , and the sample composition will then be incrementally changed at steps  70  and  72  until the sample particles have been subjected to a range of different sample compositions in which the sample particles undergo structural changes. This may entail a single composition change or several composition changes. The process  74 A illustrated in  FIG. 5  will then be modified to compute an average particle charge magnitude for each sample composition, and to compute a charge magnitude pressure profile based on the average particle charge magnitude values over the range of sample compositions. The processes  74 B and  74 C illustrated in  FIGS. 7 and 9  respectively will likewise be modified to process the charge magnitude values at the various sample compositions and in the various mass ranges. 
     In still another alternate embodiment, the particle charges may be instead be measured over reaction time range following a mixing together of two or more components to form, or alter, the sample  18 . In this embodiment, step  56  of the process  50  illustrated in  FIG. 2  will be modified to carry out a mixing together of two or more components to form the sample  18 , or to carry out a mixing together of a component to an existing mixture, and the time from initial mixing or altering will then be incrementally changed at steps  70  and  72  until the sample particles undergo a structural change or structural changes. The time passage may be short or long, and may last until the resulting mixture reaches equilibrium or some state prior to equilibrium. This embodiment may entail a single initial mixture or a series of new mixtures following an initial mixture. The process  74 A illustrated in  FIG. 5  will then be modified to compute an average particle charge magnitude over time, and to compute a charge magnitude pressure profile based on the average particle charge magnitude values over the range of time of the chemical reaction. The processes  74 B and  74 C illustrated in  FIGS. 7 and 9  respectively will likewise be modified to process the charge magnitude values at the chemical reaction time range(s) and in the various mass ranges. In still further alternate embodiments, any combination of changing sample temperature, changing sample pressure, changing sample composition and time of chemical reaction may be measured and processed each as described above. 
     While this disclosure has been illustrated and described in detail in the foregoing drawings and description, the same is to be considered as illustrative and not restrictive in character, it being understood that only illustrative embodiments thereof have been shown and described and that all changes and modifications that come within the spirit of this disclosure are desired to be protected.