METHODS AND SYSTEMS FOR PARTICLE CHARACTERISATION
20220283074 · 2022-09-08
Assignee
Inventors
- Ronnie WILLAERT (Hoeilaart, BE)
- Sandor KASAS (Pully, CH)
- Pieterjan VANDEN BOER (Etterbeek, BE)
- Anton MALOVICHKO (Lausanne, CH)
- Mitchel PEREZ GONZALEZ (Brussel, BE)
- Hichem SAHLI (Brussel, BE)
Cpc classification
C12Q1/18
CHEMISTRY; METALLURGY
G01N15/1468
PHYSICS
C12Q1/025
CHEMISTRY; METALLURGY
International classification
C12Q1/18
CHEMISTRY; METALLURGY
Abstract
A method and system for deriving particle characteristics is described. The method comprises imaging the movement of at least one free-floating particle in a liquid environment at at least one moment in time, determining for at least one moment in time a movement parameter based on the imaged movement of the free-floating particles in the liquid environment, and deriving from the movement parameter a characteristic of the at least one particle.
Claims
1. A method for deriving particle characteristics, the method comprising imaging the movement of at least one free-floating particle in a liquid environment at at least one moment in time, determining for at least one moment in time at least one movement parameter based on the imaged movement of the free-floating particles in the liquid environment, deriving from the at least one movement parameter at least one characteristic of the at least one particle.
2. The method according to claim 1, wherein the movement of the at least one free-floating particle is imaged as a function of time, and wherein the movement parameter is determined as a function of time.
3. The method according to claim 1, wherein said method comprises imaging the movement of the at least one free-floating particle at at least two moments in time, whereby at least a first imaged movement and a second imaged movement is obtained, and determining a change between the first imaged movement and the second imaged movement, and wherein said change is indicative of the movement parameter.
4. The method according to claim 3, wherein the change corresponds to a spatial displacement of the free-floating particle and/or a velocity associated with the spatial displacement of the free-floating particle and/or an acceleration associated with the spatial displacement of the free-floating particle and/or a distribution of the spatial displacement of the free-floating particle and/or a distribution of the velocity associated with the spatial displacement of the free-floating particle and/or a distribution of the acceleration associated with the spatial displacement of the free-floating particle.
5. The method according to claim 4, wherein the spatial displacement and/or the velocity associated with the spatial displacement and/or the acceleration associated with the spatial displacement and/or the distribution of the spatial displacement and/or the distribution of the velocity associated with the spatial displacement and/or the distribution of the acceleration associated with the spatial displacement is determined as a function of time.
6. The method according to claim 5, wherein at least one of: i) the spatial displacement is determined with respect to at least one spatial direction, and ii) the spatial displacement is determined with respect to two or more spatial directions.
7. The method according to claim 4, wherein an amount of the spatial displacement is determined, and wherein the characteristic of the particle is derived from said amount, and/or wherein the amount of the spatial displacement is determined within a certain period of time.
8. The method according to claim 4, wherein the spatial displacement is subjected to a space-to-frequency conversion so as to obtain one or more frequencies being associated with said spatial displacement, and wherein the characteristic of the particle is derived from said one or more frequencies.
9. The method according to claim 4, wherein the spatial displacement corresponds to a translation of the free-floating particle and/or to a rotation of the free-floating particle and/or to a deformation of the free-floating particle.
10. The method according to claim 4, wherein at least one of i) a spatial displacement of the entire free-floating particle or of at least one part of the free-floating particle is determined, ii) a spatial displacement of the entire free-floating particle is preferably determined from the spatial displacement of a centre of mass of the particle, and iii) a spatial displacement of at least one part of the free-floating particle is determined from a modification of a shape of the particle.
11. The method according to claim 4, wherein the spatial displacement is determined using a cross-correlation algorithm.
12. The method according to claim 1, wherein the movement parameter is determined from the imaged movement of a single particle, or wherein the movement parameter is determined from the imaged movement of two or more particles.
13. The method according to claim 1, further comprising selecting one or more particles for the determination of the movement parameter, wherein said selection corresponds to a manual selection and/or to an automatic selection.
14. The method according to claim 1, wherein the at least one free-floating particle is provided in a stationary liquid environment and/or in a flowing liquid environment, and/or wherein the liquid environment is subject to a forced convective flow, and/or wherein the liquid environment is provided in a limiting element such as a chamber or a reservoir or a channel or wherein the liquid environment is provided in an unlimited manner.
15. The method according to claim 1, wherein the at least one free-floating particle is subjected to one or more chemical stimuli and/or one or more physical stimuli, and wherein the movement of the at least one free-floating particle is imaged before and/or during and/or after the action of said one or more chemical stimuli and/or physical stimuli.
16. The method according to claim 1, wherein one or more compounds are added to the liquid environment, and wherein an impact of the one or more compounds on the at least one free-floating particle is derived from the movement parameter.
17. The method according to claim 16, wherein the one or more compounds are essentially immiscible with the liquid environment, and/or wherein the one or more compounds are provided in one or more further compounds, and/or wherein the one or more compounds correspond to one or more agents such as antibiotics or antifungals.
18. The method according to claim 1, wherein the movement of the at least one free-floating particle is imaged with at least one optical sensor.
19. The method according to claim 18, wherein the at least one optical sensor images the movement of the at least one free-floating particle with a frame rate of 1 microsecond or more, preferably 1 second or more, more preferably of 10 seconds or more, and/or wherein the at least one optical sensor images the movement of the at least one free-floating particle at a sub-pixel resolution.
20. The method according to claim 18, wherein the at least one optical sensor is stationary during the imaging of the movement of the at least one free-floating particle or wherein the at least one optical sensor is moved during the imaging of the movement of the at least one free-floating particle.
21. The method according to claim 1, wherein the movement of the at least one free-floating particle is imaged while being magnified by at least one magnifying device such as a microscope.
22. The method according to claim 18, wherein the liquid environment is placed directly onto the at least one optical sensor.
23. The method according to claim 1, wherein the movement parameter is determined while the movement of the particle is imaged and/or after the movement of the particle is imaged, and/or wherein the characteristic of the particle is derived from the movement parameter while the movement of the particle is imaged and/or after the movement of the particle is imaged.
24. The method according to claim 1, wherein said method comprises imaging the movement of at least one free-floating particle in a liquid environment at at least a plurality of moments in time, determining for each of said plurality of moments in time a movement parameter based on the imaged movement of the free-floating particles, and deriving from said different movement parameters a characteristic of the at least one particle.
25. The method according to claim 24, wherein the method comprises, at a plurality of moments in time, imaging the movement of at least one free-floating particle by recording a video over a predetermined period of time.
26. The method according to claim 1, wherein the at least one free-floating particle is a particle not bound to a surface of an object or to a surface of a larger particle.
27. The method according to claim 1, wherein at least one of: i) the movement is an oscillation and wherein the movement parameter is an oscillation movement parameter, and ii ) the method comprises registering different images.
28. (canceled)
29. The method according to claim 1, wherein the method comprises using a neural network and/or using a deep learning technique, and/or wherein the method comprises detection of at least one particle using a trained model.
30. (canceled)
31. The method according to claim 1, wherein the liquid environment is a diffusion environment wherein the movement of the particles is not disturbed by convection, and/or wherein the liquid environment is a natural fluid such as biological fluid, and/or wherein the liquid environment is an artificial fluid.
32. The method according to claim 1, wherein a characteristic of the at least one particle is a viability or a metabolic activity or a level of metabolic activity or a metabolic state or a vitality or a sensitivity or a resistance of the at least one particle.
33. The method according to claim 1, wherein deriving from said movement parameter a characteristic of the at least one particle comprises taking into account that the amount of movement of the particles is proportional with viability and/or a metabolic activity and/or a level of metabolic activity and/or a vitality and/or a sensitivity and/or a resistance of the at least one particle.
34. (canceled)
35. The method according to claim 1, wherein the at least one particle is at least one cell and/or at least one organelle.
36. (canceled)
37. (canceled)
38. A non-transient computer-readable medium comprising a computer program code with instructions which, when executed by a computer system, causes the computer system to carry out the method according to claim 1.
39. (canceled)
40. (canceled)
41. A system for deriving particle characteristics, the system comprising at least one optical sensor for imaging movement of at least one free-floating particle in a liquid environment at at least one moment in time, and a processor comprising an input means for obtaining the imaged movement of the at least one free-floating particle, and wherein the processor is configured to perform the method according to claim 1.
42. A method comprising using the system according to claim 41 for at least one antibiotic susceptibility testing, antifungal susceptibility testing, characterizing cell viability, metabolism monitoring, diagnostics, drug screening and antimitotic drug susceptibility testing.
43-45. (canceled)
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0139] Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. In the drawings,
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DESCRIPTION OF PREFERRED EMBODIMENTS
[0264] The present invention will be described with respect to particular embodiments and with reference to certain drawings but the invention is not limited thereto but only by the claims.
[0265] The dimensions and the relative dimensions do not correspond to actual reductions to practice of the invention.
[0266] Furthermore, the terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequence, either temporally, spatially, in ranking or in any other manner. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein.
[0267] Moreover, the terms top, under and the like in the description and the claims are used for descriptive purposes and not necessarily for describing relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other orientations than described or illustrated herein.
[0268] It is to be noticed that the term “comprising”, used in the claims, should not be interpreted as being restricted to the means listed thereafter; it does not exclude other elements or steps. It is thus to be interpreted as specifying the presence of the stated features, integers, steps or components as referred to, but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. Thus, the scope of the expression “a device comprising means A and B” should not be limited to devices consisting only of components A and B. It means that with respect to the present invention, the only relevant components of the device are A and B.
[0269] Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments.
[0270] Similarly it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
[0271] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.
[0272] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
[0273] In one aspect, the present invention can relate to a method for deriving particle characteristics. The method may comprise detecting characteristics of one particle or a plurality of particles. The particles may be any type of particles, such as in one example being cells. The method comprises imaging the movement of at least one free-floating particle in a liquid environment at at least one moment in time. The particles thereby typically do not need to be attached to a surface or to larger particles. They thus may be unattached. The imaging may be optical imaging. The imaging may be video recordings. Such video recording may be a video of the movement over a predetermined time, such as for example at least 2 seconds, at least 5 seconds or at least 10 seconds. These video recording may be repeated a plurality of times. This may correspond with different recordings spaced with predetermined time intervals between the recordings.
[0274] The method also comprises determining for at least one moment in time a movement parameter based on the imaged movement of the free-floating particles in the liquid environment. The movement parameter may be an x,y movement of a particle. The movement parameter may be an oscillation frequency of an oscillation movement of the free-floating particle. The method also comprises deriving from the movement parameter a characteristic of the at least one particle.
[0275] According to some embodiments of the present invention, for determining a movement parameter, the method may make use of an algorithm for registering images. One example of such an algorithm derivation of the movement of particles over time may make use of an algorithm for registering images such as for example described by Manuale Guizar-Sicairos et al. in Optics Letters 33, 156-158 (2008) entitled “Efficient subpixel image registration algorithms”. Alternatively also other algorithms may be used.
[0276] In some embodiments, the method may be an algorithm as illustrated in
[0277] In some embodiments use may be made of a self-learning algorithm for the automated selection of the cells before the nanomotion is measured. Use may for example be made of a neural network. In one example use may be made of a self-learning algorithm as shown in
[0278] By way of illustration, embodiments of the present invention not being limited thereto, features and advantages of an embodiment of the present invention will be illustrated below. The below example illustrates advantages of method for rapid, label-free and attachment-free characterisation of single-cell antifungal susceptibility using optical nanomotion detection.
[0279] The example shown below is framed in the dramatically increased global incidence of fungal disease in recent years. The emergence of antifungal resistant yeast strains represents nowadays a serious challenge for humanity. The development of rapid antifungal susceptibility testing (AFST) to guide treatment and clinical management decisions is one of the options to limit the spread of these organisms. In the exemplary embodiment shown below, a rapid and simple method is described that permits to assess the susceptibility of single fungal cells to antifungal drugs with very basic laboratory material. i.e. an optical microscope, a camera and a computer. Advantageously, the technique is label-free and does not require the attachment of the cells onto a substrate, which dramatically simplifies and broadens its applicability, even in remote doctor's practices in developing countries. An “optical nanomotion detection” (ONMD) method was developed that is based on cross-correlation of consecutive recorded optical images by a microscope, and it is demonstrated that the recorded “nanomotion” magnitude is proportional to the cell's activity. A deep learning single-cell detection algorithm is implemented in the video analysis pipeline to automate the detection of the individual cells amid the hundreds to thousands cells within the video sequence. Consequently, the developed method can address the nanomotion pattern of single cells as well as whole cellular populations. This methodology was applied in the present example to evaluate the antifungal susceptibility of various Candida albicans, C. glabrata and C. lusitaniae strains (including clinical strains), and the model yeast Saccharomyces cerevisiae for various antifungals. Additionally, amphotericin B dose-response curves were setup for C. albicans strains, which allowed the determination of the minimal inhibitory concentration (MIC). Apart from detecting life-death transition, the method is also sensitive to the metabolic activity of the cells as demonstrated by the correlation that was found between the nanomotion pattern of the cells and the temperature and the nutrients availability. An analysis of the yeast nanomotion pattern in the frequency domain was performed for this particular example which highlighted that the oscillation frequencies that change the most between living and death yeast cells are located between 0.5 and 4 Hz.
[0280] In the present example it is illustrated that an optical microscope equipped with a video camera can detect living cells nanometric scale oscillations. The oscillations are monitored by periodically recording (in the present example every hour) 12 s long movies of the cells in the absence and presence of a drug and processing the movies with an algorithm that can highlight sub-pixel scale movements (
[0281] The method was tested on different types of yeasts such as different Candida species and the model yeast S. cerevisiae. These Candida species can be involved in candidiasis, which is a human fungal infection that can be hard to treat due to the acquired resistance. Some of the evaluated yeast strains were hypersusceptible or resistant to the applied antifungal drugs to challenge their viability. In every case, the results corroborated with classical sensitivity tests. Antifungal dose dependence as well temperature dependence tests were carried out and demonstrated that optical nanomotion detection (ONMD) is not limited to life—death transition detection but can also monitor metabolic rate variations.
[0282] To track single cells, in the present example a cross-correlation image registration algorithm was used. The method was documented to efficiently track subtle modifications of keratin networks in living cells and displacements of myoblasts. The algorithm is based on the initial estimation of the cross-correlation peak between the first and every subsequent frame. It provides a numerical value for the image translation with a sub-pixel (sub-μm) resolution and does not require heavy calculations. Our program calculates the cell displacement for each frame and saves the trajectories of tracked cells as well as the root mean square of the displacement to an MS Excel file.
[0283] In a first set of experiments, the antimicrobial activity of ethanol on the nanomotion of various Candida strains and the model yeast S. cerevisiae was assessed. Therefore, the effect of high ethanol concentration (70%) on the x y displacements of C. albicans, C. glabrata, C. lusitaniae and S. cerevisiae cells (
[0284] In a next step, the efficiency of ONMD to perform AFST was evaluated. The x y displacement reduced significantly after treatment of the yeast cells with antifungals. As an example,
[0285] The amphotericin B dose-response curve for the C. albicans CAF2-1 wild-type strain was also explored by using the ONMD method (
[0286] In a next set of experiments, the capacity of the ONMD method to monitor metabolic changes in yeast upon chemico-physical stimulations was explored. Firstly, the effect of the temperature (in the range of 13 to 35° C.) on C. albicans, C. lusitaniae, and S. cerevisiae (
[0287] Finally, it was evaluated if the ONMD could be used to perform AFST of surface-attached cells. Therefore. C. albicans DSY1024 (sensitive). DSY294 (wild type) and DSY4814 (caspofungin resistant) were strongly attached onto a glass surface coated with concanavalin A (
[0288] Numerical analysis was performed in the frequency domain to highlight differences in the oscillation pattern occurring during the life-death transition (
[0289] In order to accelerate the data processing, a deep learning algorithm was developed that detects individual yeast cells (
[0290] In an attempt of further increasing the efficiency of the method and reduce its computational costs, additional experiments were performed with low frequency recording rates (10.5 fps instead of 83 fps). These modifications were motivated by the obtained results which demonstrated that the largest difference between living and death yeast cells occurs at frequencies lower than 4 Hz. We performed the same treatment with ethanol as before for the higher framerate acquisition of 83 fps (
[0291] Further by way of illustration, the average displacement of cells exposed to antifungals are studied and shown in
[0292] The effect of caspofungin on the average displacement of a wild type, resistant and sensitive C. albicans, and S. cerevisiae Is shown in
[0293] The experiments showing measurement movements of cells in 4 wells, illustrate features and advantages of embodiments of the present invention.
[0294] As mentioned initially, an AFM-based assay to assess the effects of chemicals on the viability of bacteria has been developed previously [8]. The detection is based on the observation that living organisms oscillate at a nanometric scale and transfer these oscillations to the AFM cantilever onto which they are attached. These oscillations stop as soon as the viability of the cells is compromised. It has been demonstrated that these oscillations are present in living bacteria, yeasts, plant and mammalian cells [9]. The nanomotions of living bacterial cells that are attached to a surface, have been confirmed by using other detection methods such as plasmonic imaging of the z-movement of bacteria [10], tracking the submicron scale x-y movement of attached bacteria [11], sensing of attached bacterial vibrations with phase noise of a resonant crystal [12], and sub-cellular fluctuation imaging [13].
[0295] As was also already mentioned, it has been noticed that an optical microscope equipped with a video camera can detect the movements of single yeast cells that are sedimented on a glass surface. Living single yeast cells show a specific cellular movement at the nanometer scale with a magnitude that is proportional to the cellular activity of the cell. We characterized this cellular nanomotion pattern of non-attached single yeast cells using classical optical microscopy. The distribution of the cellular displacements over a short time period are distinct from random movement. The range and shape of such nanomotion displacement distributions change significantly according to the metabolic state of the cell. The analysis of the nanomotion frequency pattern demonstrated that single living yeast cells oscillate at relatively low frequencies of around 2 Hz. The simplicity of the technique should open the way to numerous applications among which antifungal susceptibility tests seems the most straightforward.
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[0297] The cellular x-y movements were monitored by recording 12 s long movies (1000 frames) taken at a magnification of 400× (
[0298] In a further set of experiments, we compared single-cell nanomotions of Saccharomyces cerevisiae cells that were grown in the presence of nutrients (by growing them in YPD growth medium) to cells that were in a nutrient-free physiological PBS buffer. Single cell displacements were recorded every hour during 4 h (
[0299] The cellular nanomotions were also compared to the movements of silica beads recorded in the same conditions (
[0300] To assess the effect of the temperature on the nanomotion pattern of yeast, we monitored the cellular oscillations at different temperatures in the range of 13 to 35° C. (
[0301] Next, we characterized the cellular movements of Candida and S. cerevisiae cells when they are exposed to a killing agent. The Candida species can be involved in candidiasis, which is a human fungal infection that can be hard to treat due to the acquired resistance [17]. Some of the evaluated yeast strains were hypersusceptible or resistant to the applied antifungal drugs to challenge their viability. Firstly, we explored the effect of a high ethanol concentration (70%) on C. albicans, C. glabrata, C. lusitaniae and S. cerevisiae cells. The x-y displacements are quickly and drastically reduced after adding ethanol (
[0302] Secondly, we assessed the effect of killing of cells on the change in cellular nanomotions by exposing them to different concentration of various antifungals. The x-y displacements were significantly reduced after treatment of the cells with antifungals (
[0303] To observe the life-dead transition. C. albicans DSY294 clinical wild-type strain was exposed to lower amphotericin concentrations, including the minimal inhibitory concentration (MIC) (which has been reported for C. albicans DSY strains as 0.5 μg/ml [6, 27]. The total displacements curves show that there is an increase after 1 h for a concentration of 10 μg/ml (
[0304] To evaluate if the cells interact significantly with the glass surface, which could influence the measured displacements, the cellular nanomotions on a cell-repellent surface (PLL-PEG coated glass surface) were compared to the ones on a non-treated glass surface. Comparable results were obtained for C. albicans DSY294 and DSY1024 treated with caspofungin (
[0305] To highlight differences in the oscillation pattern occurring during the life-death transition, we performed numerical analysis in the frequency domain (
[0306] The data processing was further accelerated by using a deep learning algorithm that detects individual yeast cells (
[0307] This newly developed ONMD method for AFST used in this example was based on basic laboratory material, i.e. an optical microscope, a camera and a computer. The technique is label free and does not require the attachment of the living sample onto a substrate. It permits to rapidly determine antifungal susceptibility and metabolic activity of numerous yeast species; and can probably be extended to other microorganisms such as bacteria and to mammalian cells. The technique also permitted to highlight that living yeast cells oscillate at relatively low frequencies, i.e. below 4 Hz. The method can address the nanomotion pattern of single cells as well as whole cellular populations. ONMD has the potential to detect a single resistant cell in a large population. A limitation of the current ONMD method is that only the x-y movements are recorded. The measurement envelope could be extended by considering also the z axis movement [10], which will further increase the sensitivity of the method.
[0308] We could link the cellular nanomotions of single yeast cells to its metabolic activity by comparing the nanomotions of the cells in the presence and absence of nutrients, as well as by detecting a maximum cellular movement at the optimal growth temperature. Living single-cell nanomotions show a non-random behavior as was clear from the x-y displacements graphs and the distribution of the displacements during 1000 frames.
[0309] The nanomotion analysis of increasing amphotericin B concentration on C. albicans DSY294 and C. albicans CAF2-1 showed that the analysis based on the distribution of the displacements per frame of single cells seems to be more sensitive than those based on the total displacement of the whole cellular population. The effect of concentrations as low as the MIC of this antifungal became noticeable in a population of 20 cells. Additionally, the results showed that at amphotericin B concentrations of around 10 times the MIC, the cellular nanomotion is increased. This indicates that the mechanism of action of amphotericin B (which binds selectively to ergosterol in the cell membrane and causes the formation of pares [32]) increases the movement of the cells, i.e, the antifungal action increases the metabolic activity of the cells, probably due to an increased activity of the efflux pumps. An increase of the nanomotion (measured by the AFM-cantilever method) of the bacterium Bordetella pertussis for the antibiotic was also previously observed [33].
[0310] The analysis of the nanomotion frequency pattern demonstrated that single living yeast cells oscillate at relatively low frequencies of around 2 Hz. These results complement those published by Gimzewski and coworkers [34] who highlighted a periodical motion of the S. cerevisiae cell wall in the range of 0.8-1.6 kHz. These measurements were accomplished by AFM on yeast cells that were mechanically trapped into a filter pore. An ultrasonic excitation and interferometric motion detection permitted to detect resonance frequencies of single S. cerevisiae cells in the range of 330 kHz, which correspond to rigid body oscillations of the cell [35]. Such high frequency ranges are too high to be measured with our optical microscopy setup. Therefore, the low frequency oscillations that we observed, probably correspond to the whole-body displacements of single yeast cells. Future experiments involving high speed optical microscopy and AFM-based measurements should highlight the full spectrum of cellular oscillations and a possible contribution of low frequency cell wall oscillations.
[0311] The molecular processes that could cause the observed oscillations, have not been investigated yet in detail Additional experiments consisting in blocking or activating molecular actors (processes) would permit to better understand the observed phenomena. The nanomotion signal is made of vibrations arising from many metabolically-related sources that combine energy consumption with local movement or molecule redistributions[38]. Cellular nanomotion could arise from processes such as DNA replication, DNA transcription, protein assembly, cytoskeleton rearrangement, ionic pumps activity, organelle transport, etc. The involvement of the cytoskeleton has already been demonstrated by depolymerizing the actin cytoskeleton of osteoblasts by cytochalasin, which resulted in a reduced cellular movement (as measured by the AFM-cantilever method) [9]. Also conformational changes of proteins (as was demonstrated for human topoisomerase II [37]) could contribute to nanomotion.
[0312] By automatizing the cell recognition using a deep learning algorithm, we could avoid manual cell detection and extend the number of analysed cells and reduce the processing time. Additionally, this opens the way to analyze a larger population of cells. Future developments will include dedicated microfluidic chip development and software optimization to run the acquisition and/or the data processing steps onto a low-end computer. These developments could eventually lead to an easy operational mobile device that can be directly implemented in hospitals or even in remote doctor's practices in developing world countries where it will allow to perform antifungal susceptibility testing in the earliest possible treatment stage and make the appropriate decision for a personalized effective antifungal therapy. An alternative for the software used may in one example be MobileNet instead of the YOLO deep learning architecture, to run the acquisition and/or the data processing steps onto a low-end computer.
[0313] In the example discussed above, strains and cell growth were as follows:
[0314] AN yeast strains (Table 1) were cultured by inoculating 10 ml of YPD (10 g/l yeast extract, 20 g/l peptone, 20 g/l dextrose) medium with a colony from a YPD agar (20 g/l) plate. The cultures were grown overnight in Erlenmeyer flasks (30° C. and 200 rpm). The overnight cultures were 10- to 20-fold diluted in 5 ml YPD medium to obtain an OD.sub.600nm of 0.5 and were then allowed to grow in Erlenmeyer flasks for 1 h at 30° C. and 200 rpm. The cultures were further diluted afterwards, depending on the cell concentration (OD.sub.600nm value) to obtain an optimal number of cells for visualisation.
[0315] In the example, the optical nanomotion experiment was performed as follows:
[0316] Ten μl of each yeast cell culture was dispensed in one of the microwells, using a 4 Well FulTrac micro-Insert (ibidi, Germany) in an imaging micro-dish (Ibidi, Germany). The yeast cells were allowed to sediment for a period of 10 min before starting the measurement. The movement of cells was observed by taking movies of 1000 frames with a framerate of 84 fps using an EMCCD camera (Andor iXon, Oxford Instruments) using a Nikon TE-2000 microscope with a 40× objective. The Petri dish was kept at 30° C. using a microscope stage top incubator (ibidi, Germany).
[0317] The effect of ethanol on the viability of the cells was compared with cells grown in YPD medium as a reference. Therefore, first cell nanomotion videos were recorded and next (after approximately 1 min), 200 μl of ethanol (70% v/v) was added to the top chamber of the 4 micro-Insert wells. Videos were recorded every hour during a period of 5 h. Caspofungin, amphotericin B and fluconazole were used as antifungals in the antifungal susceptibility testing, i.e. to assess their effect on cell viability. Before starting the treatment with the antifungal, reference (no treatment) videos were recorded of yeast cells in YPD medium (time=0 h). Then (at time=1 min) 200 μl of a certain concentration of antifungal was added to the chamber (200 μl) above the 4 microwells. Measurements were taken every hour during a period of 5 h. As abiotic reference particles, silica microbeads (monodisperse silica standard, Whitehouse Scientific) with a diameter of 3 μm were used. The beads were dissolved in YPD medium and the measurements were performed at 30° C.
[0318] For the experiments where the effect of the temperature on the metabolism was evaluated, the temperature inside the microwells was controlled by adapting the temperature of water (from a recirculating water bath) circulating around the microwells. The temperature was successively adapted from 13° C. to 20, 25, 30 and finally 35° C. The yeast cells were allowed to adapt during 20 min to each temperature before measuring the nanomotion of the cells. For the experiments where the cell activity in PBS (phosphate buffered saline; 8 mg/ml NaCl, 0.20 mg/ml KCl, 1.44 mg/ml Na.sub.2HPO.sub.4, 0.24 mg/ml KH.sub.2PO.sub.4) was compared to YPD growth medium, the overnight cultured cells were 1000-fold diluted in either YPD medium or PBS, and immediately dispensed in the microwells. The nanomotions of yeast cells were measured every hour during 4 h. For the experiments with glass surface treatment, the glass surface was coated with concanavalin A (2 mg/ml. Sigma) or PLL-g-PEG (0.1 mg/ml, SuSoS AG, Switzerland) by incubating the glass surface for 30 min with the coating solutions.
[0319] The nanomotion detection software used in the current example was as follows:
[0320] The optical nanomotion detection algorithm calculates the cell displacement for each frame and saves the trajectories of tracked cells as well as the root mean square of the displacement to a MS Excel file. The main part of the program is based on the algorithm of Guizar-Sicairos et al.[14], ported from Matlab to Python in the open source image processing library sci-kit image [38]. The Python package nd2 reader (Verweij R, Online: http://www.lighthacking.nl/nd2reader/) was employed to import the videos in the ND2 Nikon format.
[0321] The Deep learning cell detection algorithm used was as follows:
[0322] The described nanomotion analysis previously described starts from the position of each individual cell, which is currently provided through a manual selection of the bounding box of a cell in the first video-frame. However, the number of videos and cells present in the videos can be considerably large, especially when determining the MIC or the impact of different temperature conditions. More importantly, providing cell detection at every few frames instead of only in the first video-frame, allows rectifying the position of those cells that drifts away from their initial positions. In these cases, manual detection efforts could span over several hours for multiframe annotations, and thus must be replaced by an automatic detection process of the cells. Therefore, we decided to use a deep learning algorithm, i.e. a medium-sized YOLO architecture, to automatize cell detection. The training process is performed using a set of 50,000 synthetic cell images randomly generated. These synthetic images, obtained using a phase contrast imaging model we previously proposed [39], look very similar compared to the cell images obtained with the microscope in terms of cells distribution, illumination and imaging artefacts. Once the YOLO model has been trained, it is then used to automatically detect cells in real microscopic images, and each detection is then used as initial position to calculate the optical nanomotion with the cross-correlation algorithm previously described. The overall processing pipeline starts from a bulk of video sequences and performs a per-frame automated single-cell detection. Next, the results are refined by averaging the cells position and detection confidence across frames to correct abrupt changes between consecutive frames. Finally, the position of cells detected with high confidence (i.e. >0.6) is provided as input to the nanomotion analysis algorithm (
[0323] Further Automation of the process was as follows:
[0324] The overall process consists of reading a sequence of frames as images containing the cells and applying the above described cell detection process every 10 frames to obtain a set of bounding boxes indicating the location of each individual cell automatically detected. Based on their positions, the cells are tracked across time as the video analysis process unfolds. At the end, in a subsequence refinement stage, one finds the contours of cells indicated by the bounding boxes in order to confirm that their centre roughly corresponds to that of the bounding boxes, and any mismatch is then used to penalize the cells detection confidence proportionally. In addition, the bounding boxes position and detection confidence is averaged across frames to avoid sharp changes the cells detection positions.
[0325] The calculation of the frequency region of optically recorded cell movements and the critical frequency was as follows:
[0326] The approach based on calculating FFT amplitude spectrum, computed for two signals of cell movements—one in the horizontal (“x”), the other in the vertical (“y”) direction—was developed to calculate the frequency region and critical frequency of optically recorded yeast cell movements. We named this method Double Region Interpolation Method (DRIM).
[0327] The idea is two perform two linear interpolations on these averaged amplitudes, in two different frequency regions. One should be done on the wideband noise part of the spectrum, sufficiently distant from the cell activity region (e.g. 5-10 Hz). The other is performed on the low frequency region, where the cell is active. Crossing point between these two straight lines determines the upper critical frequency of the cell activity region.
[0328] The approach to determine the frequency range of the cell movements is based on the direct application of the FFT algorithm on two detrended signals: one obtained for movements in the horizontal (x), the other in the vertical (y) direction. Both x and y spectra have similar profiles; however, some amplitudes were slightly different. We therefore derived their frequency-by-frequency average in order to equally incorporate frequencies of cell movements in both directions. Since signals were recorded with a sampling frequency of 83 frames/s, we visually inspected frequencies up to 41 Hz, and confirmed that cell movements are confined to a few Hz only (
[0329]
[0330] The data availability was as follows:
[0331] The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon request.
TABLE-US-00001 TABLE 1 Yeast strains used in this study. Micro- organism Type Strains Characteristics Genotype/Description Ref Candida albicans Lab Strain CAF2-1 Wild type strain Δura3::imm434/URA3 [3] Candida albicans Lab strain DSY1024 Mutant for efflux systems Δcdr1:hisG/Δcdr1::hisG; [4] Δcdr2::hisG; Δflu1::hisG/Δflu1::hisG; Δmdr1::hisG-URA-3- hisG/Δmdr1::hisG Candida albicans Lab strain DSY2621 Mutant for efflux systems, Δcdr1:hisG/Δcdr1::hisG; [5] sensitive to stress Δcdr2::hisG; Δflu1::hisG/Δflu1::hisG; Δmdr1::hisG/Δmdr1::hisG; Δcna::hisG-URA3-hisG Candida albicans Clinical strain DSY294 Azole-susceptible strain Wild type [2] Candida albicans Clinical strain DSY296 Azole-resistant strain Mutant for TAC1 and ERG11 [2] Candida albicans Clinical strain DSY4614 Candin-resistant strain FSK1 mutant P649H [7] Candida glabrata Clinical strain DSY562 Azole-susceptible strain Wild type [6] Candida lusitaniae Clinical strain DSY4606 Wild type strain Wild type [7] Candida lusitaniae Clinical strain DSY4590 FKS1, S638Y, erg4 Resistant FKS1 mutant S638Y, [7] to candins and amphotericin B loss of function in ERG4 Saccharomyces Lab strain BY4742 Wild type strain MATα his3Δ1 leu2Δ0 [1] cerevisiae lys2Δ0 ura3Δ0 [0332] [1] Brachmann C B. 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