CN110687191A - Microorganism identification and typing method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR (Fourier transform infrared spectroscopy) spectrum combination - Google Patents
Microorganism identification and typing method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR (Fourier transform infrared spectroscopy) spectrum combination Download PDFInfo
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Abstract
The invention discloses a microorganism identification and typing method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR spectroscopy. The method comprises the following specific steps: (1) respectively collecting a microorganism characteristic mass spectrum and an infrared absorption spectrum; (2) preprocessing the collected characteristic mass spectrum and the collected infrared absorption spectrum; (3) performing data fusion on the preprocessed characteristic mass spectrum and infrared absorption spectrum data; (4) and performing cluster analysis on the data obtained after the fusion of the mass spectrum and the infrared absorption spectrum to realize the identification and typing of the microorganisms. The data set for typing microorganisms of the invention contains more characteristic biological information, and can realize effective differentiation of strains which are difficult to differentiate by extremely similar and simple means, such as Escherichia coli and Shigella.
Description
Technical Field
The invention belongs to the technical field of microbial detection and analysis, and particularly relates to a microbial identification and typing method based on the combination of matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR spectroscopy.
Background
The microbial identification system of matrix-assisted laser analysis time-of-flight mass spectrometry in the last five years has wide clinical application, because the system has huge time and cost advantages compared with the traditional culture and biochemical identification mass spectrometry, and the rapid and accurate microbial identification or typing can provide guidance for the clinical treatment of doctors in time. At present, three hospitals in China are basically equipped with a microorganism identification system of mass-assisted laser analysis time-of-flight mass spectrometry. The identification of the microorganisms by mass spectrum is mainly based on the difference of microbial ribosomal proteins, and ribosomal proteins with different masses are presented as specific fingerprint spectra in the mass spectrum. The ribosomal protein of the microorganism has high conservation, but has certain specificity among species, so the ribosomal protein can be used for identifying the species of the microorganism. More than 4000 strains of microorganisms were included in the existing commercial databases and the identification accuracy could reach 90%. However, for some very similar bacteria, such as Escherichia coli and Shigella (FIG. 1), the mass spectrum peaks of their abundant ribosomal proteins are very similar, and the existing microorganism identification systems cannot effectively distinguish them.
Since different biological materials have different corresponding wavenumber ranges in the infrared absorption spectrum (fig. 2): the lipid content is 3000-2800cm-1(ii) a The protein amide I band and the protein amide II band are 1700-1500cm-1(ii) a Phospholipid, DNA and RNA of 1500-1185cm-1The polysaccharide content is 1200-900cm-1And 900--1Of the fingerprint area not yet identified. The infrared absorption spectrum contains a lot of biological information, has natural selectivity, and the species, type and even plant difference of the microorganism can be reflected on the infrared absorption spectrum of the microorganism, so the infrared absorption spectrum is a very potential tool for identifying and typing the microorganism. However, the collection of the infrared signal of the microorganisms is often severely disturbed by water. The most serious interference is in the wave number range of the protein, the absorption peak of the protein is seriously overlapped with the absorption of water, and the region of the protein can be removed only when the method is used for bacteria identification and typing in order to reduce the interference of the water as much as possible. The protein is the most important component of the microorganism except water and carries a great deal of biological information, and the lack of the protein signal necessarily has certain influence on the identification and typing of the microorganism.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention discloses a microorganism identification and typing method based on the combination of matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF) and Fourier transform infrared spectroscopy (FTIR); the method disclosed by the invention identifies and types the microorganisms in a mode of fusing matrix-assisted laser desorption ionization time-of-flight mass spectrometry data with Fourier infrared absorption spectrum data, wherein the data fusion mode simultaneously covers biological signals which are provided by mass spectra and derived from microbial ribosomal proteins and biological signals which are provided by infrared absorption spectra and derived from nucleic acids, polysaccharides and esters; further, more biological information is effectively utilized, the difference among microorganisms is highlighted, and the capability of identifying and typing the microorganisms and the stability of the method are further improved.
The technical scheme of the invention is specifically introduced as follows.
A microorganism identification and typing method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR spectroscopy combined use comprises the following specific steps:
(1) respectively collecting a microorganism characteristic mass spectrum and an infrared absorption spectrum;
(2) preprocessing the collected characteristic mass spectrum and the collected infrared absorption spectrum;
(3) performing data fusion on the preprocessed characteristic mass spectrum and infrared absorption spectrum data;
(4) and performing cluster analysis on the data obtained after the fusion of the mass spectrum and the infrared absorption spectrum to realize the identification and typing of the microorganisms.
In the invention, in the step (1), the method for respectively collecting the microorganism characteristic mass spectrum and the infrared absorption spectrum specifically comprises the following steps:
1) culturing the strain frozen at-80 ℃ by a culture medium for collecting subsequent mass spectrum and infrared spectrum data;
2) for mass spectrometry, selecting bacteria on a culture medium by using an inoculating loop, suspending the bacteria in absolute ethyl alcohol, and then breaking the walls of the bacteria sample and extracting protein; then, spotting the protein extracting solution on a target plate, covering a layer of matrix solution after the protein extracting solution is naturally dried, and collecting mass spectrum data after the matrix is dried; collecting mass spectrum signals with mass-to-charge ratio of 2000-20000 daltons in a linear positive ion mode;
3) for infrared spectroscopy, selecting bacteria from a culture medium by using an inoculating loop, suspending the bacteria in 0.9% sodium chloride solution, coating the bacteria on a calcium fluoride wafer, placing the calcium fluoride wafer in a drying oven, and continuously drying the calcium fluoride wafer until a layer of thin film is formed on the surface of the calcium fluoride wafer by using a bacterial suspension; fixing the wafer on an infrared spectrometer, namely collecting infrared spectrum; the light source selects near infrared light, and the scanning wave number range is 500-4000 cm-1。
In the invention, in the step (2), the method for preprocessing the collected characteristic mass spectrum and the collected infrared absorption spectrum specifically comprises the following steps:
1) for mass spectrum, carrying out Savitzky-Golay smoothing, baseline calibration, repeated multiple arrangement of technology and extraction and normalization of mass spectrum peaks on collected mass spectrum data, and using peak data for subsequent data fusion;
2) for infrared spectroscopy, background subtraction is performed over the full spectral range, followed by clipping of the wavenumber range 900-4000cm-1Adjusting the baseline, zeroing both ends of the spectrogram, and adjusting the wave number range of 1800-2800cm-1Adjusting to be flush with the zero position in a sectional mode; fitting and smoothing the single spectrogram by adopting a Savitzky-Golay filter to obtain a first derivative and a second derivative of the original spectrogram; then, the obtained infrared absorption spectrogram, first derivative spectrogram and second derivative spectrogram are normalized and subjected to wave number area subdivision, and the wave number range of 900-1200cm in which the polysaccharide is positioned is extracted-1For subsequent data fusion.
In the invention, in the step (3), the collected characteristic mass spectrum and infrared absorption spectrum data are subjected to data fusion
The method comprises the following specific steps:
the wave number range of the polysaccharide in the infrared spectrum is 900-1200cm-1The data set of (a) is fused with the data set of mass spectral peaks with mass-to-charge ratio in the range of 2000-.
In the invention, in the step (4), a method for carrying out cluster analysis on data obtained by fusing mass spectrum and infrared absorption spectrum
The method specifically comprises the following steps: performing quality screening on the formed data set, removing the data set if the effective numerical value missing proportion of the data set is more than fifty percent, replacing the data set by half of the minimum value in the original data if numerical value missing occurs in the data set, and filtering the data by using a quartile bit spacing IQR; and carrying out quantile normalization processing on the data, carrying out logarithmic transformation on the data, and then carrying out hierarchical clustering HCA analysis, thereby achieving the purposes of identification and typing.
Compared with the prior art, the invention has the beneficial effects that: the data set for typing microorganisms of the invention contains more characteristic biological information, and can realize effective differentiation of strains which are difficult to differentiate by extremely similar and simple means, such as Escherichia coli and Shigella.
Drawings
FIG. 1, mass spectrum of microorganism.
FIG. 2 is a graph showing the infrared absorption spectrum (A) and the wave number range of 900--1Second derivative spectrum (B).
FIG. 3, results of hierarchical clustering analysis (tryptone soy agar (TSA) solid medium culture); a is the result of using the single mass spectrum data for cluster analysis, B is the result of using the single infrared absorption spectrum data for cluster analysis, and C is the result of using the mass spectrum and infrared absorption spectrum fused data set for cluster analysis. The green line in the graph represents the threshold line, the nodes where the threshold line intersects the dendrogram, and the nodes marked with small red points are regarded as correct clusters.
FIG. 4, hierarchical clustering analysis results (Columbia blood agar solid medium culture); a is the result of using the single mass spectrum data for cluster analysis, B is the result of using the single infrared absorption spectrum data for cluster analysis, and C is the result of using the mass spectrum and infrared absorption spectrum fused data set for cluster analysis. The green line in the graph represents the threshold line, the nodes where the threshold line intersects the dendrogram, and the nodes marked with small red points are regarded as correct clusters.
FIG. 5, hierarchical clustering analysis results (LB broth culture); a is the result of using the single mass spectrum data for cluster analysis, B is the result of using the single infrared absorption spectrum data for cluster analysis, and C is the result of using the mass spectrum and infrared absorption spectrum fused data set for cluster analysis. The green line in the graph represents the threshold line, the nodes where the threshold line intersects the dendrogram, and the nodes marked with small red points are regarded as correct clusters.
FIG. 6 is a statistical chart of the accuracy of cluster analysis under different culture conditions.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto. Meanwhile, in order to verify the stability of the method, bacterial samples under different culture conditions are also tried.
1. Mass spectrum sample preparation method
The direct smearing method (common method) comprises smearing bacteria to be tested on the center of target spot of target plate with one end of aseptic wooden cotton swab or toothpick, picking single colony as far as possible, picking several pure colonies if the colony is small, smearing into uniform film, covering with 0.6 ~ 1 μ L formic acid (for breaking cell wall), drying, covering with 1uL matrix, drying to form crystal (light yellow film), and detecting with instrument.
A formic acid-acetonitrile extraction method (suitable for most microorganisms) includes adding 300 microliter of pure water into 1.5 mL Eppendorf tube, taking a proper amount of 5-10mg of fresh bacteria into the tube by using a disposable inoculating loop, uniformly shaking, adding 900 microliter of absolute ethyl alcohol, uniformly mixing, centrifuging (12000 r/min, 2 ~ 3 min), discarding the ethyl alcohol, centrifuging again to completely discard the supernatant, adding 50 microliter of 70% formic acid and 50 microliter of acetonitrile into the Eppendorf tube, sufficiently shaking, centrifuging (12000 r/min, 3 min), taking 1 microliter of supernatant, spotting on a target plate, drying the sample at room temperature, covering with 1 microliter of matrix, drying at room temperature, and putting into an instrument for detection.
2. Hierarchical clustering is coacervate hierarchical clustering. The strategy is to treat each object as a cluster, and then combine two clusters with the minimum distance into a new cluster by taking the Euclidean distance as the basis. The distance between the new clusters is then recalculated, and the two clusters with the smallest distance are merged into one cluster, and the process loops until all objects are in one cluster.
EXAMPLE 1 typing of Escherichia coli and Shigella (tryptone Soy agar (TSA) solid Medium culture)
1. Respectively collecting microbial characteristic mass spectrum and infrared absorption spectrum
1) And (3) culturing the strain frozen at-80 ℃ for 14 hours in a TSA solid culture medium-based 37 ℃ incubator for subsequent collection of mass spectrum and infrared spectrum data. The bacterial culture method has no specificity, only needs to be suitable for bacterial growth and ensures the consistency of culture conditions as much as possible in the whole identification process so as to reduce the interference of unknown factors brought by different culture modes.
2) For mass spectrometry, an appropriate amount of bacteria is picked from a solid medium by an inoculating loop and resuspended in an ethanol solution, and then the bacterial sample is subjected to wall breaking and protein extraction by a formic acid-acetonitrile extraction method (for general bacteria, a direct coating method can be adopted, and for bacteria containing spores or fungi, a trifluoroacetic acid extraction method is selected). And (3) dropping 1 microliter of extracting solution on a target plate, covering a layer of matrix solution after the extracting solution is naturally dried, and collecting mass spectrum data after the matrix is dried. Mass spectral signals were collected in the linear positive ion mode in the mass to charge ratio range of 2000-20000 daltons (the laser used was a nitrogen 377 nm laser).
3) For infrared spectroscopy, an appropriate amount of bacteria was picked from the solid medium with an inoculating loop, resuspended in 0.9% sodium chloride solution, 50 microliters was spread on calcium fluoride wafers with a pipette, placed in a 45 ℃ oven, and dried for 30 minutes. The bacterial suspension forms a film on the surface of the wafer and is fixed on the wafer. The wafer is fixed on an infrared spectrometer, and then infrared spectrum collection can be carried out. The sample preparation method has no specificity, and only needs to ensure that the solid culture medium is not introduced into the sample in the sample preparation process so as to reduce the interference of the culture medium; ensuring that the bacterial suspension can form a solid film fixed on the surface of the wafer. The light source selects near infrared light when the spectrogram is collected, and the scanning wave number range is 500-4000 cm-1Resolution of spectrogram is 4cm-1The number of scanning cycles was 62 and the scanning speed was 21 times per minute.
2. Mass spectrum and infrared absorption spectrum pretreatment:
1) for mass spectra, the collected mass spectra data were subjected to Savitzky-Golay smoothing, baseline calibration, multiple permutations of technique repetition, and extraction and normalization of mass spectral peaks. Using the peak data for subsequent data fusion;
2) for infrared spectroscopy, background subtraction is performed over the full spectral range, followed by clipping of the wavenumber range 900-4000cm-1A baseline adjustment is performed. Zeroing both ends of the spectrum, and simultaneously setting the wave number range of 1800 and 2800cm-1And adopting segmented adjustment to be flush with the zero position. And fitting and smoothing the single spectrogram by adopting a Savitzky-Golay filter to obtain a first derivative and a second derivative of the original spectrogram. Then, the obtained infrared absorption spectrogram, first derivative spectrogram and second derivative spectrogram are normalized and subjected to wave number area subdivision, for example, the wave number range of 900-1200cm where the polysaccharide is located is extracted-1. For subsequent data fusion.
3. Performing data fusion on the collected characteristic mass spectrum and the collected infrared absorption spectrum: the wave number range of the polysaccharide in the second derivative spectrogram is 900-1200cm-1The data set of (a) is fused with the data set of mass spectral peaks with mass-to-charge ratio in the range of 2000-.
4. Performing cluster analysis on data obtained after the mass spectrum and the infrared absorption spectrum are fused: and (3) performing quality screening on the formed data set, removing the data set if the effective numerical value missing proportion of the data set is more than fifty percent, replacing the data set by half of the minimum value in the original data if numerical value missing occurs in the data set, and filtering the data by using a four-quadrant spacing (IQR). The data is subjected to quantile normalization processing, meanwhile, the data is subjected to logarithm transformation, and then Hierarchical Clustering (HCA) is carried out, and the result is shown in figure 3. The green line in fig. 3 is the threshold line and the red dots are labeled as correctly clustered clusters. Wherein FIG. 3A shows the results of cluster analysis using MALDI-TOF data alone, and 19 strains in total, 10 strains among which were correctly clustered, and the cluster accuracy was 10/19, FIG. 3B shows the results of cluster analysis using FTIR data alone, and 19 strains among which 16 strains among them were correctly clustered, and the cluster accuracy was 16/19, and FIG. 3C shows the results of cluster analysis using MALDI-TOF and FTIR data fused, and 19 strains among which were correctly clustered, and the cluster accuracy was 100%. The MALDI-TOF is independently used for typing escherichia coli and shigella, and the accuracy is low; FTIR is used for typing of colibacillus and shigella separately, and the typing accuracy is superior to MALDI-TOF; and compared with the MALDI-TOF and FTIR data fusion, the accuracy of typing by using the MALDI-TOF and FTIR data alone is further improved; the microorganism identification and typing method based on the combination of MALDI-TOF and FTIR spectrum has the capability of microorganism typing which is not ignored.
Example 2
Typing of Escherichia coli and Shigella (Columbia blood agar solid medium culture) (see FIG. 4 for cluster analysis results) the green line in FIG. 4 is the threshold line and the red dots are labeled as correctly clustered clusters. Wherein FIG. 4A shows the results of cluster analysis using MALDI-TOF data alone, and 19 strains in total, and 9 strains among them were correctly clustered, and the cluster accuracy was 9/19, FIG. 4B shows the results of cluster analysis using FTIR data alone, and 19 strains among them, and 16 strains among them were correctly clustered, and the cluster accuracy was 17/19, and FIG. 4C shows the results of cluster analysis using MALDI-TOF and FTIR data fused, and 19 strains among them were correctly clustered, and the cluster accuracy was 100%. The MALDI-TOF is independently used for typing escherichia coli and shigella, and the accuracy is low; FTIR is used for typing of colibacillus and shigella separately, and the typing accuracy is superior to MALDI-TOF; and the accuracy of the MALDI-TOF and FTIR data fusion is further improved compared with the accuracy of the MALDI-TOF and FTIR data used for typing alone.
Example 3
Typing of Escherichia coli and Shigella (LB broth culture) (see FIG. 5 for cluster analysis results) the green line in FIG. 5 is the threshold line and the red dots are marked as correctly clustered clusters. Wherein FIG. 5A shows the results of cluster analysis using MALDI-TOF data alone, and 19 strains in total, of which 9 strains were correctly clustered and the cluster accuracy was 9/19, FIG. 4B shows the results of cluster analysis using FTIR data alone, and 19 strains in total, of which 15 strains were correctly clustered and the cluster accuracy was 1519, and FIG. 4C shows the results of cluster analysis using MALDI-TOF and FTIR data fused, and of which 19 strains in total were correctly clustered and the cluster accuracy was 100%. The MALDI-TOF is independently used for typing escherichia coli and shigella, and the accuracy is low; FTIR is used for typing of colibacillus and shigella separately, and the typing accuracy is superior to MALDI-TOF; and the accuracy of the MALDI-TOF and FTIR data fusion is further improved compared with the accuracy of the MALDI-TOF and FTIR data used for typing alone.
FIG. 6 is a statistical chart of the accuracy of cluster analysis under different culture conditions. The results show that under the three culture conditions, the accuracy of the technical scheme for performing cluster analysis on the data obtained by fusing the mass spectrum and the infrared absorption spectrum is higher than that of the technical scheme for performing cluster analysis on the mass spectrum or the infrared absorption spectrum alone.
Claims (5)
1. A microorganism identification and typing method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and FTIR spectroscopy is characterized by comprising the following specific steps:
(1) respectively collecting a microorganism characteristic mass spectrum and an infrared absorption spectrum;
(2) preprocessing the collected characteristic mass spectrum and the collected infrared absorption spectrum;
(3) performing data fusion on the preprocessed characteristic mass spectrum and infrared absorption spectrum data;
(4) and performing cluster analysis on the data obtained after the fusion of the mass spectrum and the infrared absorption spectrum to realize the identification and typing of the microorganisms.
2. The method for identifying and typing a microorganism according to claim 1, wherein: in the step (1), the method for respectively collecting the microbial characteristic mass spectrum and the infrared absorption spectrum specifically comprises the following steps:
1) culturing the strain frozen at-80 ℃ by a culture medium for collecting subsequent mass spectrum and infrared spectrum data;
2) for mass spectrometry, selecting bacteria on a culture medium by using an inoculating loop, suspending the bacteria in absolute ethyl alcohol, and then breaking the walls of the bacteria sample and extracting protein; then, spotting the protein extracting solution on a target plate, covering a layer of matrix solution after the protein extracting solution is naturally dried, and collecting mass spectrum data after the matrix is dried; collecting mass spectrum signals with mass-to-charge ratio of 2000-20000 daltons in a linear positive ion mode;
3) for infrared spectroscopy, selecting bacteria from a culture medium by using an inoculating loop, suspending the bacteria in 0.9% sodium chloride solution, coating the bacteria on a calcium fluoride wafer, placing the calcium fluoride wafer in a drying oven, and continuously drying the calcium fluoride wafer until a layer of thin film is formed on the surface of the calcium fluoride wafer by using a bacterial suspension; fixing the wafer on an infrared spectrometer, namely collecting infrared spectrum; the light source selects near infrared light, and the scanning wave number range is 500-4000 cm-1。
3. The method for identifying and typing a microorganism according to claim 1, wherein: in the step (2), the method for preprocessing the collected characteristic mass spectrum and the collected infrared absorption spectrum specifically comprises the following steps:
1) for mass spectrum, carrying out Savitzky-Golay smoothing, baseline calibration, repeated multiple arrangement of technology and extraction and normalization of mass spectrum peaks on collected mass spectrum data, and using peak data for subsequent data fusion;
2) for infrared spectroscopy, background subtraction is performed over the full spectral range, followed by clipping of the wavenumber range 900-4000cm-1Adjusting the baseline, zeroing both ends of the spectrogram, and adjusting the wave number range of 1800-2800cm-1Adjusting to be flush with the zero position in a sectional mode; fitting and smoothing the single spectrogram by adopting a Savitzky-Golay filter to obtain a first derivative and a second derivative of the original spectrogram; then, the obtained infrared absorption spectrogram, first derivative spectrogram and second derivative spectrogram are normalized and subjected to wave number area subdivision, and the wave number range of 900-1200cm in which the polysaccharide is positioned is extracted-1For subsequent data fusion.
4. The method for identifying and typing a microorganism according to claim 1, wherein: in the step (3), the method for performing data fusion on the collected characteristic mass spectrum and infrared absorption spectrum data specifically comprises the following steps:
the wave number range of the polysaccharide in the infrared spectrum is 900-1200cm-1The data set of (a) is fused with the data set of mass spectral peaks with mass-to-charge ratio in the range of 2000-.
5. The method for identifying and typing a microorganism according to claim 1, wherein: in the step (4), the method for performing cluster analysis on the data obtained by fusing the mass spectrum and the infrared absorption spectrum specifically comprises the following steps: performing quality screening on the formed data set, removing the data set if the effective numerical value missing proportion of the data set is more than fifty percent, replacing the data set by half of the minimum value in the original data if numerical value missing occurs in the data set, and filtering the data by using a quartile bit spacing IQR; and carrying out quantile normalization processing on the data, carrying out logarithmic transformation on the data, and then carrying out hierarchical clustering HCA analysis, thereby achieving the purposes of identification and typing.
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