Search

We, the participants of the First Organoid Intelligence Workshop - "Forming an OI Community" (22-24 February 2022), call on the international scientific community to explore the potential of human brain-based organoid cell cultures to advance our understanding of the brain and unleash new forms of biocomputing while recognizing and addressing the associated ethical implications. The term "organoid intelligence" (OI) has been coined to describe this research and development approach (1) in a manner consistent with the term "artificial intelligence" (AI) - used to describe the enablement of computers to perform tasks normally requiring human intelligence. OI has the potential for diverse and far-reaching applications that could benefit humankind and our planet, and which urge the strategic development of OI as a collaborative scientific discipline. OI holds promise to elucidate the physiology of human cognitive functions such as memory and learning. It presents game-changing opportunities in biological and hybrid computing that could overcome significant limitations in silicon-based computing. It offers the prospect of unparalleled advances in interfaces between brains and machines. Finally, OI could allow breakthroughs in modeling and treating dementias and other neurogenerative disorders that cause an immense and growing disease burden globally. Realizing the world-changing potential of OI will require scientific breakthroughs. We need advances in human stem cell technology and bioengineering to recreate brain architectures and to model their potential for pseudo-cognitive capabilities. We need interface breakthroughs to allow us to deliver input signals to organoids, measure output signals, and employ feedback mechanisms to model learning processes. We also need novel machine learning, big data, and AI technologies to allow us to understand brain organoids.

Matrix assisted laser desorption ionization (MALDI) is a powerful technique that improved the mass spectrometry (MS) characterization of biological molecules. However this technique requires the mixing of matrix compound with the analyte of interest. The matrix compound used in MALDI process is not universal and usually depends heavily on the nature of analyte of interest being analyzed. As such there are many matrices that are used and without knowing the nature of your analyte it will be hard to predict which matrix is optimal for the most effective MALDI-MS analysis. Moreover, a high energy laser exposure is needed to initiate the ionization process through a charge transfer process between the matrix and analyte molecules. Recent advancement in the metalorganic framework (MOF) field introduced desirable surfaces that can be modified for various applications. Such MOFs can be synthesized with porous solid, and could have regular or predicted geometry. This project is introducing a novel idea of utilizing a modified MALDI substrate with MOF that can provide charge transfer between immobilized functionalized groups and analyte molecules that mimic the solvation process when a solution matrix is used. Begin the abstract two lines below author names and addresses.

We report the occurrence of Bacillus amyloliquefaciens in vanilla orchids (Vanilla phaeantha) and cultivated hybrid vanilla (V. planifolia × V. pompona) as a systemic bacterial endophyte. We determined with light microscopy and isolations that tissues of V. phaeantha and the cultivated hybrid were infected by a bacterial endophyte and that shoot meristems and stomatal areas of stems and leaves were densely colonized. We identified the endophyte as B. amyloliquefaciens using DNA sequence data. Since additional endophyte-free plants and seed of this orchid were not available, additional studies were performed on surrogate hosts Amaranthus caudatus, Ipomoea tricolor, and I. purpurea. Plants of A. caudatus inoculated with B. amyloliquefaciens demonstrated intracellular colonization of guard cells and other epidermal cells, confirming the pattern observed in the orchids. Isolations and histological studies suggest that the bacterium may penetrate deeply into developing plant tissues in shoot meristems, forming endospores in maturing tissues. B. amyloliquefaciens produced fungal inhibitors in culture. In controlled experiments using morning glory seedlings we showed that the bacterium promoted seedling growth and reduced seedling necrosis due to pathogens. We detected the gene for phosphopantetheinyl transferase (sfp), an enzyme in the pathway for production of antifungal lipopeptides, and purified the lipopeptide "surfactin" from cultures of the bacterium. We hypothesize that B. amyloliquefaciens is a robust endophyte and defensive mutualist of vanilla orchids. Whether the symbiosis between this bacterium and its hosts can be managed to protect vanilla crops from diseases is a question that should be evaluated in future research.

The extracellular proteins (ECPs) of enterohemorrhagic Escherichia coli (EHEC) can cause hemorrhagic colitis which may cause life threatening hemolytic-uremic syndrome, while that of enteroaggregative E. coli (EAEC) can clump to intestinal membranes. Liquid chromatography-electrospray ionization-tandem mass spectrometry based proteomics is used to evaluate a preliminary study on the extracellular and whole cell protein extracts associated with E. coli strain pathogenicity. Proteomics analysis, which is independent of genomic sequencing, of EAEC O104:H4 (unsequenced genome) identified a number of proteins. Proteomics of EHEC O104:H4, causative agent of the Germany outbreak, showed a closest match with E. coli E55989, in agreement with genomic studies. Dendrogram analysis separated EHEC O157:H7 and EHEC/EAEC O104:H4. ECP analysis compared to that of whole cell processing entails few steps and convenient experimental extraction procedures. Bacterial characterization results are promising in exploring the impact of environmental conditions on E. coli ECP biomarkers with a few relatively straightforward protein extraction steps.

A "one-pot" alternative method for processing proteins and isolating peptide mixtures from bacterial samples is presented for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and data reduction. The conventional in-solution digestion of the protein contents of bacteria is compared to a small disposable filter unit placed inside a centrifuge vial for processing and digestion of bacterial proteins. Each processing stage allows filtration of excess reactants and unwanted byproduct while retaining the proteins. Upon addition of trypsin, the peptide mixture solution is passed through the filter while retaining the trypsin enzyme. The peptide mixture is then analyzed by LC-MS/MS with an in-house BACid algorithm for a comparison of the experimental unique peptides to a constructed proteome database of bacterial genus, specie, and strain entries. The concentration of bacteria was varied from 10 × 10(7) to 3.3 × 10(3) cfu/mL for analysis of the effect of concentration on the ability of the sample processing, LC-MS/MS, and data analysis methods to identify bacteria. The protein processing method and dilution procedure result in reliable identification of pure suspensions and mixtures at high and low bacterial concentrations.

Whole cell protein and outer membrane protein (OMP) extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest neighbor database organism strains. These extracts were isolated from pathogenic and nonpathogenic strains of Yersinia pestis and Escherichia coli using ultracentrifugation and a sarkosyl extraction method followed by protein digestion and analysis using liquid chromatography tandem mass spectrometry (MS). Whole cell protein extracts contain many different types of proteins resident in an organism at a given phase in its growth cycle. OMPs, however, are often associated with virulence in Gram-negative pathogens and could prove to be model biomarkers for strain differentiation among bacteria. The mass spectra of bacterial peptides were searched, using the SEQUEST algorithm, against a constructed proteome database of microorganisms in order to determine the identity and number of unique peptides for each bacterial sample. Data analysis was performed with the in-house BACid software. It calculated the probabilities that a peptide sequence assignment to a product ion mass spectrum was correct and used accepted spectrum-to-sequence matches to generate a sequence-to-bacterium (STB) binary matrix of assignments. Validated peptide sequences, either present or absent in various strains (STB matrices), were visualized as assignment bitmaps and analyzed by the BACid module that used phylogenetic relationships among bacterial species as part of a decision tree process. The bacterial classification and identification algorithm used assignments of organisms to taxonomic groups (phylogenetic classification) based on an organized scheme that begins at the phylum level and follows through the class, order, family, genus, and species to the strain level. For both Gram-negative organisms, the number of unique distinguishing proteins arrived at by the whole cell method was less than that of the OMP method. However, the degree of differentiation measured in linkage distance units on a dendrogram with the OMP extract showed similar or significantly better separation than the whole cell protein extract method between the sample and correct database match compared to the next nearest neighbor. The nonpathogenic Y. pestis A1122 strain used does not have its genome available, and thus, data analysis resulted in an equal similarity index to the nonpathogenic 91001 and pathogenic Antiqua and Nepal 516 strains for both extraction methods. Pathogenic and nonpathogenic strains of E. coli were correctly identified with both protein extraction methods, and the pathogenic Y. pestis CO92 strain was correctly identified with the OMP procedure. Overall, proteomic MS proved useful in the analysis of unique protein assignments for strain differentiation of E. coli and Y. pestis. The power of bacterial protein capture by the whole cell protein and OMP extraction methods was highlighted by the data analysis techniques and revealed differentiation and similarities between the two protein extraction approaches for bacterial delineation capability.

Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.

Raman chemical imaging microspectroscopy is evaluated as a technology for waterborne pathogen and bioaerosol detection. Raman imaging produces a three-dimensional data cube consisting of a Raman spectrum at every pixel in a microscope field of view. Binary and ternary mixtures including combinations of polystyrene beads, gram-positive Bacillus anthracis, B. thuringiensis, and B. atrophaeus spores, and B. cereus vegetative cells were investigated by Raman imaging for differentiation and characterization purposes. Bacillus spore aerosol sizes were varied to provide visual proof for corroboration of spectral assignments. Conventional applications of Raman imaging consist of differentiating relatively broad areas of a sample in a microscope field of view. The spectral angle mapping data analysis algorithm was used to compare a library spectrum with experimental spectra from pixels in the microscope field of view. This direct one-to-one matching is straightforward, does not require a training set, is independent of absolute spectral intensity, and only requires univariate statistics. Raman imaging is expanded in its capabilities to differentiate and distinguish between discrete 1-6 microm size bacterial species in single particles, clusters of mixed species, and bioaerosols with interference background particles.

To gain insights into the toxicity induced by the nerve agent VX, an MS-based phosphoproteomic analysis was carried out on the piriform cortex region of brains from VX-treated rats. Using isobaric tag based TMT labeling followed by titanium dioxide enrichment strategy, we identified 9975 unique phosphosites derived from 3287 phosphoproteins. Temporal changes in the phosphorylation status of peptides were observed over a time period of 24 h in rats exposed to a 1× LD50, intravenous (i.v.) dose with the most notable changes occurring at the 1 h postexposure time point. Five major functional classes of proteins exhibited changes in their phosphorylation status: (i) ion channels/transporters, including ATPases, (ii) kinases/phosphatases, (iii) GTPases, (iv) structural proteins, and (v) transcriptional regulatory proteins. This study is the first quantitative phosphoproteomic analysis of VX toxicity in the brain. Understanding the toxicity and compensatory signaling mechanisms will improve the understanding of the complex toxicity of VX in the brain and aid in the elucidation of novel molecular targets that would be important for development of improved countermeasures. All MS data have been deposited in the ProteomeXchange with identifier PXD001184 (http://proteomecentral.proteomexchange.org/dataset/PXD001184).

We report the occurrence of Bacillus amyloliquefaciens in vanilla orchids (Vanilla phaeantha) and cultivated hybrid vanilla (V. planifolia × V. pompona) as a systemic bacterial endophyte. We determined with light microscopy and isolations that tissues of V. phaeantha and the cultivated hybrid were infected by a bacterial endophyte and that shoot meristems and stomatal areas of stems and leaves were densely colonized. We identified the endophyte as B. amyloliquefaciens using DNA sequence data. Since additional endophyte-free plants and seed of this orchid were not available, additional studies were performed on surrogate hosts Amaranthus caudatus, Ipomoea tricolor, and I. purpurea. Plants of A. caudatus inoculated with B. amyloliquefaciens demonstrated intracellular colonization of guard cells and other epidermal cells, confirming the pattern observed in the orchids. Isolations and histological studies suggest that the bacterium may penetrate deeply into developing plant tissues in shoot meristems, forming endospores in maturing tissues. B. amyloliquefaciens produced fungal inhibitors in culture. In controlled experiments using morning glory seedlings we showed that the bacterium promoted seedling growth and reduced seedling necrosis due to pathogens. We detected the gene for phosphopantetheinyl transferase (sfp), an enzyme in the pathway for production of antifungal lipopeptides, and purified the lipopeptide "surfactin" from cultures of the bacterium. We hypothesize that B. amyloliquefaciens is a robust endophyte and defensive mutualist of vanilla orchids. Whether the symbiosis between this bacterium and its hosts can be managed to protect vanilla crops from diseases is a question that should be evaluated in future research.

Bioaerosols are airborne particles that may contain pathogenic species that can cause serious risks to various government and public sectors. The major health concern due to bioaerosols is that certain communicable diseases are transmitted through airborne particles, including viruses, bacteria, and fungi. Biological warfare agents can be disseminated as bioaerosol particles and could pose severe safety issues for military operations as well as serious economic and health concerns to the public. Thus, it is imperative to develop and implement real-time detection and accurate identification technologies for the monitoring of bioaerosols. Mass spectrometry (MS) techniques have been developed and improved in their sensitivity, fieldability, and compatibility to bioaerosol analysis and characterization in real-time settings. MS techniques have shown promise in the real-time analysis of bioaerosols. An overview of bioaerosol MS is presented for general perspectives on its application for detection and identification capabilities. Also, the capabilities of MS techniques and the nature of their output and impact on the detection and identification of bioaerosols will be discussed. Exploration of the advantages and drawbacks of the applications for different MS techniques in the analysis of bioaerosols is addressed.

The secreted proteins of the enterohemorrhagic and enteropathogenic E. coli (EHEC and EPEC) are the most common cause of hemorrhagic colitis, a bloody diarrhea with EHEC infection, which often can lead to life threatening hemolytic-uremic syndrome (HUS).We are employing a metaproteomic approach as an effective and complimentary technique to the current genomic based approaches. This metaproteomic approach will evaluate the secreted proteins associated with pathogenicity and utilize their signatures as differentiation biomarkers between EHEC and EPEC strains. The result showed that the identified tryptic peptides of the secreted proteins extracted from different EHEC and EPEC growths have difference in their amino acids sequences and could potentially utilized as biomarkers for the studied E. coli strains. Analysis of extract from EHEC O104:H4 resulted in identification of a multidrug efflux protein, which belongs to the family of fusion proteins that are responsible of cell transportation. Experimental peptides identified lies in the region of the HlyD haemolysin secretion protein-D that is responsible for transporting the haemolysin A toxin. Moreover, the taxonomic classification of EHEC O104:H4 showed closest match with E. coli E55989, which is in agreement with genomic sequencing studies that were done extensively on the mentioned strain. The taxonomic results showed strain level classification for the studied strains and distinctive separation among the strains. Comparative proteomic calculations showed separation between EHEC O157:H7 and O104:H4 in replicate samples using cluster analysis. There are no reported studies addressing the characterization of secreted proteins in various enhanced growth media and utilizing them as biomarkers for strain differentiation. The results of FY-2012 are promising to pursue further experimentation to statistically validate the results and to further explore the impact of environmental conditions on the nature of the secreted biomarkers in various E. coli strains that are of public health concerns in various sectors.

Raman microspectroscopy and principal component analysis are used to decipher unique biomolecular information by monitoring the effect of residence time of Bacillus spores suspended in deionized water. Suspensions of viable spores of Bacillus anthracis Sterne (BA), Bacillus atrophaeus (BG), and Bacillus thuringiensis were prepared and spectrally monitored from initial deposition (time zero) and intermittently for seven days. Questions addressed include if spectral variations are significant with bacterial species and residence time under non-germination conditions, is the discrimination capability affected, and are there markers indicating pre-germination activity. Clear spectral distinction for the spore suspensions was observed with respect to residence time, however, when the residence time data were combined, discrimination analyses showed significant overlap between the BA and BG spores. Temporal spectral analyses at select wavenumbers suggest an increase in pre-germination activity from the freshly suspended to one day suspensions.

Mass spectrometry based proteomic approaches are showing promising capabilities in addressing various biological and biochemical issues. Outer membrane proteins (OMPs) are often associated with virulence in gram-negative pathogens and could prove to be excellent model biomarkers for strain level differentiation among bacteria. Whole cells and OMP extracts were isolated from pathogenic and non-pathogenic strains of Francisella tularensis, Burkholderia thailandensis, and Burkholderia mallei. OMP extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest-neighbor database strains. This study addresses the comparative experimental proteome analyses of OMPs vs. whole cell lysates on the strain-level discrimination among gram negative pathogenic and non-pathogenic strains.

Bacterial proteome represents the collection of functional and structural proteins that are present in the cell. The bacterial proteome consists of diverse classes of proteins with different cellular functions. Overall, the protein content of the cell represents the majority of the cell dry weight, which makes it an ideal cellular component to be utilized for bacterial characterization. Comparative proteomics has been widely applied to microbial identification and characterization studies through the utilization of several mass spectrometry techniques, with tandem mass spectrometry techniques proving to be effective and reliable approach. This chapter will address the utilization of comparative proteomics and the application of tandem mass spectrometry in the identification and differentiation of bacterial strains.

We have developed suite of bioinformatics algorithms for automated identification and classification of microbes based on comparative analysis of protein sequences. This application uses sequence information of microbial proteins revealed by mass spectrometry-based proteomics for identification and phyloproteomics classification. The algorithms transforms results of searching product ion spectra of peptide ions against a protein database, performed by commercially available software (e.g. SEQUEST), into a taxonomically meaningful and easy to interpret output. To achieve this goal we constructed a custom protein database composed of theoretical proteomes derived from all fully sequenced bacterial genomes (1204 microorganisms as of August 25th, 2010) in a FASTA format. Each protein sequence in the database is supplemented with information on a source organism and chromosomal position of each protein coding open reading frame (ORF) is embedded into the protein sequence header. In addition this information is linked with a taxonomic position of each database bacterium. ABOid analyzes SEQUEST search results files to provide the probabilities that peptide sequence assignments to a product ion mass spectrum (MS/MS) are correct and uses the accepted spectrum–to-sequence matches to generate a sequence-to-organism (STO) matrix of assignments. Because peptide sequences are differentially present or absent in various strains being compared this allows for the classification of bacterial species in a high throughput manner. For this purpose, STO matrices of assignments, viewed as assignment bitmaps, are next analyzed by a ABOid module that uses phylogenetic relationships between bacterial species as a part of decision tree process, and by applying multivariate statistical techniques (principal component and cluster analysis), to reveal relationship of the analyzed unknown sample to the database microorganisms. Our bacterial classification and identification algorithm uses assignments of an analyzed organism to taxonomic groups based on an organized scheme that begins at the phylum level and follows through classes, orders, families and genus down to strain level.

BackgroundIn 2010 Colony Collapse Disorder (CCD), again devastated honey bee colonies in the USA, indicating that the problem is neither diminishing nor has it been resolved. Many CCD investigations, using sensitive genome-based methods, have found small RNA bee viruses and the microsporidia, Nosema apis and N. ceranae in healthy and collapsing colonies alike with no single pathogen firmly linked to honey bee losses.Methodology/principal findingsWe used Mass spectrometry-based proteomics (MSP) to identify and quantify thousands of proteins from healthy and collapsing bee colonies. MSP revealed two unreported RNA viruses in North American honey bees, Varroa destructor-1 virus and Kakugo virus, and identified an invertebrate iridescent virus (IIV) (Iridoviridae) associated with CCD colonies. Prevalence of IIV significantly discriminated among strong, failing, and collapsed colonies. In addition, bees in failing colonies contained not only IIV, but also Nosema. Co-occurrence of these microbes consistently marked CCD in (1) bees from commercial apiaries sampled across the U.S. in 2006-2007, (2) bees sequentially sampled as the disorder progressed in an observation hive colony in 2008, and (3) bees from a recurrence of CCD in Florida in 2009. The pathogen pairing was not observed in samples from colonies with no history of CCD, namely bees from Australia and a large, non-migratory beekeeping business in Montana. Laboratory cage trials with a strain of IIV type 6 and Nosema ceranae confirmed that co-infection with these two pathogens was more lethal to bees than either pathogen alone.Conclusions/significanceThese findings implicate co-infection by IIV and Nosema with honey bee colony decline, giving credence to older research pointing to IIV, interacting with Nosema and mites, as probable cause of bee losses in the USA, Europe, and Asia. We next need to characterize the IIV and Nosema that we detected and develop management practices to reduce honey bee losses.

Raman microspectroscopy is used to probe the age and milieu parameters for suspensions of bacteria for their detection in water backgrounds. No studies have been reported on the fate of Raman signatures over time for biologicals stored in water matrices. A FALCON II Raman Chemical Imaging System (ChemImage, Pittsburgh, PA) and 532 nm laser excitation source acquired the Raman spectra. MATLAB principal components (PC) analysis software was employed for data reduction. Suspensions of Bacillus atrophaeus, Bacillus thuringiensis , and three strains of E. coli (EC) were prepared in distilled and recipe tap water. Aliquots at 5 min, 5 hr, and 1, 2, and 7 days at 25 C were dried on microscope slides in replicate. Adequate spectral differences were observed for all three organism species. Microscope analysis showed that freshly suspended Bacillus spores and EC vegetative cells, in both water matrices, remained as spores after seven days. Agar plate growth procedures showed that the bacteria were still viable even after seven days resting in both water matrices. All three bacterial species were separated based on PC analysis; however, the three EC strains coalesced. The water matrix parameter was inconsistent in its ability to separate the Raman spectra in PC plots of the five bacteria. Within each group, the time parameter poorly separated the bacterial resting suspensions as the aging proceeded. A Mahalanobis linkage distance analysis (dendrogram) for all three species and strains in both water matrices confirmed a random order for all five suspension times.

In this study, we demonstrate the effect of sample matrix composition of MS2 virus on its characterization by ESI-MS and IVDS. MS2 samples grown and purified using various techniques showed different responses on ESI-MS than that on IVDS. The LC-MS of the specific biomarker of MS2 bacteriophage from an infected Escherichia coli sample was characterized by the presence of E. coli proteins. The significant impact of sample matrix was observed upon identification of MS2 using a database search. Infected E. coli with MS2 showed a matching score indifferent from uninfected ones. Only purified MS2, using CsCl and analyzed by LS-MS, showed a positive match using the database search. However, the variation in MS2 sample matrix had no effect on the deification of MS2.

A system and method for detecting the presence of submicron sized particles in a sample taken from the environment. More particularly, the system may be used to detect and identify bacteria by detecting the presence of bacterial pili which have been separated from bacterial cells in the sample. The system includes means for collecting a sample from the environment, separating pili from bacteria in the sample, and purifying and concentrating the submicron sized pili in the sample based on the size of the pili. The purified and concentrated pili are detected with an apparatus which includes an electrospray assembly having an electrospray capillary, a differential mobility analyzer which receives output from the capillary, and a condensation particle counting device for counting the number of pili sized particles that pass through the differential mobility analyzer.

Raman chemical imaging microspectroscopy (RCIM) is being evaluated as a technology for waterborne pathogen detection. Binary and ternary mixtures including combinations of polystyrene beads, Grampositive Bacillus anthracis and B. atrophaeus spores, B. cereus vegetative cells, and Gram-negative E. coli cells were investigated by RCIM for differentiation and characterization purposes. We have demonstrated the ability of RCIM, in combination with Pearson's cross correlation and multivariate principal components analysis data reduction techniques, to differentiate these components in the same field of view (FOV). Conventional applications of RCIM consist of differentiating relatively broad areas in a FOV. Here, RCIM is expanded in its capabilities to differentiate and distinguish between different micron size species in single particles and clusters of mixed species.

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection. We have investigated the impact of key experimental and background interference parameters on the bacterial species level identification performance of Raman detection. These parameters include laser-induced photodamage threshold, composition of water matrix, and organism aging in water. The laser-induced photodamage may be minimized by operating a 532 nm continuous wave laser excitation at laser power densities below 2300 W/cm2 for Gram-positive Bacillus atrophaeus (formerly Bacillus globigii, BG) vegetative cells, 2800 W/cm2 for BG spores, and 3500 W/cm2 for Gram-negative E. coli (EC) organisms. In general, Bacillus spore microorganism preparations may be irradiated with higher laser power densities than the equivalent Bacillus vegetative preparations. In order to evaluate the impact of background interference and organism aging, we selected a biomaterials set comprising Gram-positive (anthrax simulants) organisms, Gram-negative (plague simulant) organisms, and proteins (toxin simulants) and constructed a Raman signature classifier that identifies at the species level. Subsequently, we evaluated the impact of tap water and storage time in water (aging) on the classifier performance when characterizing B. thuringiensis spores, BG spores, and EC cell preparations. In general, the measured Raman signatures of biological organisms exhibited minimal spectral variability with respect to the age of a resting suspension and water matrix composition. The observed signature variability did not substantially degrade discrimination performance at the genus and species levels. In addition, Raman chemical imaging spectroscopy was used to distinguish a mixture of BG spores and EC cells at the single cell level.

Principal component analysis (PCA) of the normal Raman and surface-enhanced Raman (SER) spectra provides species level identification of bacteria. SERS provides improved discrimination over normal Raman.

Raman chemical imaging microscopy was used to visualize and discriminate between biological substances with Raman spectral database identification. Water suspensions were investigated for mixtures of different concentrations of E. coli and Bacillus subtilis spores.

Raman microspectroscopy is investigated for spectral information from bacteria and protein biological substances in distilled and tap water matrices. Laser energy flux, multivariate dataspace biological differentiation, and substance aging in water matrices were investigated.

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection and the fidelity of the Raman spectra of microorganisms with respect to their differentiation at the single cell level are investigated. Individual entities are investigat ed in the microscope field of view (FOV) by Raman chemical imaging microscopy (RCIM) . The size of a substance was not found to cause spectral confusion when collating individual entities in the FOV by multivariate principal components (PCA) and RCIM methods. Polystyrene (PS) beads in 1-3 micron sizes were collectively grouped t ogether by PCA. Distilled and recipe tap water matrices produced the proper identification of the PS beads throughout the FOV, and all PS beads in a FOV were grouped togeth er by PCA. A mixture of Gram-positive Bacillus atrophaeus spores and Gram-negative E. coli cells were differentiated and distinguished by RCIM. Index Headings: Raman chemical imag ing microspectroscopy; Bacillus atrophaeus ; E. coli ; Principal components analysis; Water matrix; Biological discrimination; Bacteria; Recipe tap water.

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.