LENS-FREE HOLOGRAPHIC OPTICAL SYSTEM FOR HIGH SENSITIVITY LABEL-FREE CELL AND MICROBIAL GROWTH DETECTION AND QUANTIFICATION FOR SCREENING, IDENTIFICATION, AND SUSCEPTIBILITY TRAINING
Disclosed are optical interrogation apparatus that can produce lens-free images using an optoelectronic sensor array to generate a holographic image of sample objects, such as microorganisms in a sample. Also disclosed are methods of detecting and/or identifying microorganisms in a biological sample, such as microorganisms present in low levels. Also disclosed are methods of using systems to detect microorganisms in a biological sample, such as microorganisms present in low levels. In addition or as an alternative, the methods of using systems may identify microorganisms present in a sample and/or determine antimicrobial susceptibility of such microorganisms.
1 . An automated system comprising:
a. an automated holographic optical apparatus situated to determine the phenotypical behavior of an object in a sample based on a detected variation over time of a hologram of the sample;
b, wherein the holographic optical apparatus is an in-line holographic apparatus and the hologram is an in-line hologram;
c. Wherein the in-line holographic optical apparatus includes one or a plurality of reference beam sources situated to direct the reference beam(s) to the sample volume, a sample receptacle situated to hold the sample volume in view of the reference beam(s), an optical sensor situated to detect the in-line hologram formed by the reference beam(s) and the sample volume, and a controller coupled to the optical sensor and that includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram; and
d. an output of at least one data calculation module, and a phenotypical behavior of the cell unit, wherein the phenotypical behavior of the cell unit is classified based on the detected variation.
2 . The method of claim 1 , further comprising the output of the at least one data calculation mode is determined by a raw hologram imaging processing data calculation module to calculate a variability metric between time-lapse images.
3 . The method of claim 2 , further comprising the variability metric is calculated not using holographic image reconstruction by Fourier transformation.
4 . The method of claim 1 , further comprising that the at least one data calculation module contains a deeply supervised convolutional neural network.
5 . An in-line holographic optical system comprising:
a) a reference beam source;
b) a sample receptacle below the reference beam source;
c) an optical sensor below the sample receptacle; and
d) a controller coupled to the optical sensor.
6 . The apparatus of claim 5 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine a variation over time of an in-line hologram.
7 . An in-line holographic system comprising:
a) a reference beam source;
b) an illumination source adjacent to the reference beam source;
c) a sample receptacle below the illumination source;
d) an optical sensor below the sample receptacle; and
e) a hologram controller coupled to the optical sensor.
8 . The apparatus of claim 7 , wherein the illumination source is a single illumination source.
9 . The apparatus of claim 7 , wherein the illumination source comprises more than one illumination source.
10 . The apparatus of claim 7 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to filter the hologram directly.
11 . The apparatus of claim 7 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to reconstruct the hologram image.
12 . The apparatus of claim 7 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to remove uninformative noise or background from the hologram image.
13 . The apparatus of claim 7 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to identify growth in independent subsections.
14 . The apparatus of claim 7 , wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to globalize the local signal.
15 . An automated system, comprising: an automated holographic optical apparatus situated to determine at least antimicrobial susceptibility of a microorganism corresponding to an object in a sample volume based on a detected variation over time of a hologram of the sample volume, an output of at least one data calculation module, and a phenotypical behavior of the microorganism.
16 . The system of claim 15 , wherein the phenotypical behavior of the microorganism is classified based on the detected variation and the output of the at least one data calculation module.
17 . An in-line holographic optical system comprising a reference beam source situated to direct a reference beam to the sample volume, a sample receptacle situated to hold the sample volume in view of the reference beam, an optical sensor situated to detect the in-line hologram formed by the reference beam and the sample volume, and a controller coupled to the optical sensor and that includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram.
18 . The system of claim 17 further comprising, an output of at least one data calculation module, and a phenotypical behavior of the cell unit, wherein the phenotypical behavior of the cell unit is classified based on the detected variation.
19 . A system for tracking a detected variation over time, comprising:
a. a light source;
b. an optical sensor below the light source; and
c. a hologram controller coupled to the optical sensor the wherein the controller includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine a variation over time of an in-line hologram.
20 . An automated system, comprising: an automated in-line holographic optical apparatus situated to detect variation over time of an in-line hologram of a sample volume.
21 . The system of claim 20 , wherein the variation over time of the in-line hologram is calculated using a data calculation module.
22 . A computer-implemented machine for characterizing a plurality of particles, comprising
a. a processor; and
b. a tangible computer-readable medium operatively connected to the processor and including computer code configured to:
i) generate an in-line hologram of a first particle of the plurality of particles at a first time; and
ii) generate an in-line hologram of a second particle of the plurality of particles at a second time; and
iii) determine a variation over time of the in-line hologram.
23 . The machine of claim 22 , wherein the variation over time of the in-line hologram is calculated using a data calculation module.
24 . A computer-implemented machine for differentiating a plurality of particles from bacteria, comprising
a. a processor; and
b. a tangible computer-readable medium operatively connected to the processor and including computer code configured to:
i) generate an in-line hologram of a first particle of the plurality of particles at a first time;
ii) generate an in-line hologram of a first bacteria of the plurality of bacteria at a first time
iii) generate an in-line hologram of a second particle of the plurality of particles at a second time;
iv) generate an in-line hologram of a second bacteria of the plurality of bacteria at a second time;
v) differentiating a plurality of particles from bacteria based on a variation over time of the in-line hologram.
25 . The machine of claim 24 , wherein the variation over time of the in-line hologram is calculated using a data calculation module.