Using an optical microscope to perform maturity classification of stained reticulocytes

Abstract

The present invention relates to the use of light microscopy for reticulocyte maturity classification of stained reticulocytes. In particular, the present invention relates to a method for reticulocyte maturity classification from a whole blood sample, comprising: staining the sample with a supravital agglutination stain reagent or a fluorescent agglutination dye; illuminating the stained sample with a light beam to detect reticulocytes; determining for each reticulocyte the parameters (i) the fraction of reticular area (Ar) relative to the whole cell area (Ac) (Λ) and (ii) the fraction of reticular perimeter (Ur) relative to the reticular area (Ar) (Γ); and classifying the reticulocyte maturity into one of four main maturity classes according to the determined Λ and Γ values.

Description

Technical Field

[0001] This invention relates to a method for maturity classification of reticulocytes from whole blood samples, comprising: staining the sample with a supravitalagglutinating dyeing reagent or a fluorescent agglutinating dye; illuminating the stained sample with a light beam to detect reticulocytes; determining parameters for each reticulocyte: (i) the fraction (Λ) of the reticulum area (Ar) relative to the whole cell area (Ac) and (ii) the fraction (Γ) of the perimeter of the reticulum (Ur) relative to the reticulum area (Ar); and classifying the reticulocyte maturity into one of four main maturity categories based on the determined Λ and Γ values. Background Technology

[0002] Monitoring erythropoiesis is crucial for determining a patient's health status and response to different treatments, such as in the recovery period from iron deficiency anemia or chemotherapy. During erythropoiesis, as late-stage erythroblasts lose their nuclei, the developing cells are subsequently called reticulocytes, which contain a network of filamentous RNA, known as the reticular formation. Reticulocytes, obtained from peripheral blood, are readily available and indicate erythrocyte (RBC) proliferation by measuring cell count and maturation (Piva et al., 2015, Clinics in Laboratory Medicine, 35, 133-163). Two main methods are commonly used for detecting reticulocytes: agglutination fluorescence staining or ultravivo staining. Both staining methods aggregate fine filamentous RNA chains into a network of filaments, which can be observed under a microscope.

[0003] While automated hematology analyzers measure reticulocyte parameters more precisely, accurately, and reproducibly than manually counting cells under an optical microscope, each analyzer uses different reagents, which exhibit varying sensitivities to binding with RNA and other cellular components. Therefore, across different staining methods, there is no universal classification for determining reticulocyte maturity (Van Den Bossche et al., 2002, Clin. Chem. Lab. Med., 40(1), 69-73). A unified classification method is adopted by determining the immature reticulocyte fraction (IRF) using fluorescence methods. The IRF is the fraction of less immature reticulocytes compared to the total number of reticulocytes (Heimpel et al., 2010, Med. Klin., 105, 538-543). However, because no consensus has been found for each category, the IRF also varies between different analyzers (Riley et al., 2001, J. Clin. Labor. Anal., 15, 267-294). A different classification method is based on Ludwig Heilmeyer's work in the 1930s, which defines four mature categories, including immature reticulocytes with dense reticular tissue (category 1), reticulocytes with extensive but loose reticular networks (category 2), reticulocytes with scattered reticular tissue networks (category 3), and mature reticulocytes with scattered reticular tissue granules (category 4) (Riley et al., 2001, J. Clin. Labor. Anal., 15, 267-294).

[0004] Due to the differences between various automated hematology analyzers, manual counting under an optical microscope remains the gold standard for diagnosing the maturity class of reticulocytes. There is also increasing interest in devices that determine all parameters of a complete blood count, devices that are optically similar to an optical microscope.

[0005] Therefore, a standardized metric is needed to analyze stained reticulocytes, which can be linked to manual, and especially automated, microscopic analysis as well as derived diagnostic tests. Summary of the Invention

[0006] This invention addresses this need and provides a method for classifying the maturity of reticulocytes from whole blood samples, comprising: (a) staining the sample using a super-vitaminizing staining reagent or a fluorescent agglutinating dye; (b) irradiating the stained sample with a light beam of preferably 200 nm to 780 nm in a light detection device, preferably a microscope, to detect reticulocytes; (c) determining the parameters for each reticulocyte: (i) the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) and (ii) the fraction (Γ) of the reticular perimeter (Ur) relative to the reticular area (Ar); and (d) classifying the maturity of the reticulocytes into one of four main maturity categories based on the determined Λ and Γ values. This method advantageously allows for the classification of reticulocytes according to standardized metrics, which is largely independent of the platform or device used, and thus allows for a uniform allocation of reticulocytes according to the well-established Heilmeyer classification scheme familiar to most practitioners. The novel method according to the invention further allows for improvements in diagnostic readings based on time-dependent changes in reticulocyte counts after treatment or during disease progression.

[0007] In a preferred embodiment of the invention, the method further includes the step of enumerating (calculating) reticulocytes as a final step. Particularly preferred is that the enumeration is performed by category and by sample.

[0008] In a further preferred embodiment, staining is performed using a super-living agglutination staining agent selected from NMB (new methylene blue), Brilliant cresol blue, crystal violet, methyl violet, and Nile blue. In yet another preferred embodiment, staining is performed using agglutination fluorescent dye selected from acridine orange, auramine O, D-methyloxacarbocyanide, ethidium bromide, Pyronin Y, Thioflavin-T, and thiazole orange.

[0009] The staining may optionally further include a chemical nucleic acid crosslinking step, preferably using nitrogen mustard, cis-diamminedichloroplatinum(II) or a derivative thereof, or chloroethyl nitrosourea (CENU).

[0010] In another embodiment, step (c) described above is performed using a device including an imaging module. Preferably, the imaging module is designed to perform a morphological segmentation operation.

[0011] A further embodiment relates to a method as defined above, which additionally includes the following step as step (e): morphologically comparing each stained reticulocyte with an image repository of reticulocytes that have been independently classified by experts. Preferably, the classification results in the labeling of reticulocytes whose morphological classification differs from that of step (d).

[0012] In a preferred embodiment, the morphological comparison includes applying images of stained reticulocytes to a machine-learning-based method trained using images from the image repository of reticulocytes.

[0013] In one embodiment of the method according to the invention, maturity classification includes assigning reticulocytes to category 1, 2, 3, or 4 based on a ratio Λ / Γ. In a preferred embodiment, a Λ / Γ value of about >2.5 indicates category 1, a Λ / Γ value of about 1 to about 2.5 indicates category 2, a Λ / Γ value of about 0.35 to about 1 indicates category 3, and a Λ / Γ value of about <0.35 indicates category 4.

[0014] In a further aspect, the present invention relates to a computer-implemented method for classifying the maturity of reticulocytes from one or more images captured from whole blood samples, comprising determining within the images parameters (i) the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) for each reticulocyte and (ii) the fraction (Γ) of the reticular perimeter (Ur) relative to the reticular area (Ar); and classifying the maturity of the reticulocytes into four main categories based on the Λ and Γ values. The method preferably further includes the step of enumerating reticulocytes.

[0015] In another aspect, the present invention relates to an in vitro method for monitoring and determining the health status of a subject and / or the subject’s response to treatment, comprising a method for classifying the maturity of reticulocytes as defined above using one or more whole blood samples obtained from said subject.

[0016] In a preferred embodiment of the in vitro method for monitoring and determining the health status of a subject and / or the subject's response to treatment, when the initial sample is compared with a second or further sample collected from the subject after 2, 3, 4, 5, 6, 7 days or more, an increase in the number of reticulocytes in category 2, 3 or 4, preferably category 3 or 4, more preferably category 4, indicates an improvement in the health status of the subject in low reticulocyte disease and / or a positive response to treatment, or indicates a deterioration in the health status of the subject in high reticulocyte disease and / or a negative response to treatment.

[0017] In a further aspect, the present invention relates to a device comprising means for performing a method for classifying the maturity of reticulocytes as defined above or an in vitro method as defined above for monitoring and determining the health status of a subject and / or the subject’s response to treatment.

[0018] In another aspect, the present invention relates to a data processing apparatus, including devices for performing a computer-implemented method for classifying the maturity of reticulocytes from one or more images as defined above.

[0019] In a final aspect, the present invention relates to a computer program comprising instructions which, when executed by a computer, cause the computer to perform a method for classifying the maturity of reticulocytes as defined above, including a morphological comparison step, or a computer-implemented method as defined herein.

[0020] It should be understood that, without departing from the scope of the invention, the above features and the features to be explained below can be used not only in the corresponding combinations shown, but also in other combinations or individually. Attached Figure Description

[0021] Figure 1Quantitative maturation classification of reticulocytes recorded in bright-field transmission mode according to the present invention is shown. The classification is based on the percentage (Λ) of the area of ​​intracellular stained reticulum relative to the total cell area and the fraction (Γ) of the perimeter of all RNA fragments relative to their area. Four reticulocyte classifications proposed by Heilmeyer are indicated. Category 1 (closed circle (1)) is the least mature category, with an example cell shown in (2). Category 2 (open circle with dots (3)) corresponds to the second youngest (second least mature) reticulocyte population (with an example cell shown in (4)), followed by Category 3 (open circle (5)) with an example cell (6), and Category 4 (dashed circle (7); example cell (8)) is the most mature population.

[0022] Figure 2 An example of a bright-field transmission color (9) image of stained reticulocytes divided into separate RGB (R: red (10), G: green (11), B: blue (12)) 8-bit images is shown. Cell size was determined by using Otsu-thresholding on the red channel. For the area determination of the reticular tissue, since the cell wall only appears in the blue channel, a mask of stained RNA was generated by subtracting the green channel from the blue channel. An automatic thresholding in ImageJ was further applied to the generated image to obtain a mask of stained reticular tissue (13).

[0023] Figure 3 It shows in Figure 1 The figure shows different descriptions of quantitative maturation classification of reticulocytes recorded in the bright-field transmission mode of the present invention. In this figure, the Λ / Γ ratio is shown in comparison to the number of cells (14). The figure shows cells of category 1 (closed circle (1)), category 2 (open circle (3) with dots), category 3 (open circle (5)) and category 4 (dashed circle (7)) according to the Heilmeyer scheme. Detailed Implementation

[0024] Although the invention will be described with respect to specific embodiments, such description should not be construed as limiting.

[0025] Before describing exemplary embodiments of the present invention in detail, definitions that are important for understanding the present invention are given.

[0026] Unless the context clearly specifies otherwise, the singular forms “an” and “a” as used in this specification and the appended claims also include the corresponding plural forms.

[0027] In the context of this invention, the term "about" refers to an accuracy range that, as those skilled in the art will understand, still ensures the technical effect of the features in question. This term typically indicates a deviation of ±25% from the indicated value. In specific embodiments, the term also indicates deviations of ±15%, ±10%, ±5%, ±3%, ±2%, ±1%, or ±0.5% from the indicated value.

[0028] It should be understood that the term "comprising" is not limiting. For the purposes of this invention, the terms "consisting of" or "essentially consisting of" are considered preferred embodiments of the term "comprising of". If a group is defined below as comprising at least a certain number of embodiments, this means that a group preferably consisting only of those embodiments is also covered.

[0029] In addition, the terms “(i)”, “(ii)”, “(iii)” or “(a)”, “(b)”, “(c)”, “(d)” or “first”, “second”, “third”, etc., in the specification or claims are used to distinguish between similar elements and are not necessarily used to describe a successive or chronological order.

[0030] It should be understood that the terms used so far are interchangeable where appropriate, and the embodiments of the invention described herein can be operated in a different order than that described or illustrated herein. If a term relates to a method, procedure, or step used, there is no temporal or time interval continuity between the steps; that is, unless otherwise stated, these steps may be performed simultaneously, or there may be time intervals of seconds, minutes, hours, days, weeks, etc., between these steps.

[0031] It should be understood that the present invention is not limited to the specific methods, protocols, etc., described herein, as these can be modified. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention, which is limited only by the appended claims.

[0032] The accompanying drawings should be considered schematic illustrations and the elements shown in the drawings are not necessarily shown to scale. Rather, the various elements are shown in such a way that their function and general purpose will be obvious to those skilled in the art.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0034] As described above, in one aspect, the present invention relates to a method for classifying the maturity of reticulocytes from whole blood samples, comprising: (a) staining the sample with an ultra-vivo agglutination staining reagent or a fluorescent agglutination dye; (b) irradiating the stained sample with a light beam of a preferred wavelength range of 200 nm to 780 nm in a light detection device, preferably a microscope, to detect reticulocytes; (c) determining parameters for each reticulocyte: (i) the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) and (ii) the fraction (Γ) of the reticular perimeter (Ur) relative to the reticular area (Ar); and (d) classifying the maturity of the reticulocytes into one of four main maturity categories based on the determined Λ and Γ values.

[0035] As used herein, the term "whole blood sample" refers to a mammalian, preferably human, blood sample obtained from a subject by suitable methods known to those skilled in the art. Samples used in the context of this invention should preferably be collected in a clinically acceptable manner, more preferably in a manner that preserves nucleic acids, particularly RNA. "Whole blood" primarily comprises erythrocytes and precursor cells, leukocytes and precursor cells, and platelets suspended in plasma. In certain specific embodiments, the samples are pooled.

[0036] The present invention preferably envisions the use of non-coalesced samples. In specific embodiments of the invention, the contents of whole blood samples may also be submitted to specific processing steps. For example, the samples may be diluted or enriched. Additionally, nucleic acid stabilizers or anti-degradation agents may be added. In a particularly preferred embodiment, the use of anticoagulants such as EDTA is envisioned. In a further specific embodiment, the whole blood samples may undergo an initial cell sorting or cell separation step prior to staining. Such steps are envisioned to result in the enrichment and / or purification of erythrocytes (including precursor cells, such as reticulocytes), or the enrichment and / or purification of reticulocytes.

[0037] The method according to the invention envisions a “staining” step using a whole blood sample as defined above as the first step. This step can be performed using any suitable staining agent capable of revealing the area and perimeter of the reticular tissue. Thus, the staining agent is able to at least partially stabilize nucleic acid structures, particularly RNA structures in cells, and is able to display these structures under suitable optical conditions, preferably when illuminated. As used herein, the term “reticular tissue” refers to a network of nucleic acids (particularly RNA, typically ribosomal RNA that becomes visible under staining conditions). The reticular tissue is the specific structure that distinguishes reticulocytes from other blood cells. As used herein, “reticulocyte” refers to immature red blood cells without a nucleus. During erythropoiesis, reticulocytes mature in the bone marrow, then circulate in the bloodstream for about a day before developing into mature red blood cells. In healthy subjects, the fraction of reticulocytes in the blood of adults is typically about 0.5% to 2.5%, and in infants about 2% to 6%. The number of reticulocytes in a whole blood sample is often used as an indicator of bone marrow activity because it represents the most recent erythropoietic event.

[0038] Because it allows for the presence of nucleic acids in the reticular tissue that distinguishes reticulocytes from mature erythrocytes and other cells, staining is preferably performed using a staining agent that provides an agglutination effect and allows for appropriate contrast in optical detection procedures. In a preferred embodiment of the invention, this staining method is based on an agglutination staining reagent.

[0039] As used herein, the term "agglutination staining reagent" refers to a staining agent presumably that binds to intracellular structures containing nucleic acids, particularly ribosomes, and causes them to clump together. In a preferred embodiment, the invention contemplates the use of all suitable agglutination staining reagents having this function or capability.

[0040] In a preferred set of embodiments, the agglutination staining agent is a super-vitamin staining agent. "Super-vitamin" dyes are commonly used to stain live cells taken from an organism. Preferred examples of super-vitamin staining agents include NMB (neo-methylene blue), leucosyl blue, crystal violet, methyl violet, and Nile blue. The use of any suitable derivatives or functional equivalents thereof is further contemplated. The use of Azure B or any suitable derivative thereof is also contemplated. The invention further contemplates the use of super-vitamin staining agents that may not yet be developed and which satisfy the functions described above. The use of NMB (neo-methylene blue) is particularly preferred. Further information is available from suitable literature resources, such as, for example, Samuel M. Rapoport, 2019, Theticulocyte, 1 st ed., CRC Press.

[0041] In another preferred embodiment, the agglutination staining agent is a fluorescent agglutination staining agent. This dye also interacts with structures containing nucleic acids, particularly ribosomes, and provides fluorescence upon exposure to suitable excitation light. Examples of such staining agents include acridine orange, auramine O, D-methyloxacarbonyl cyanine, ethidium bromide, pyronin Y, thiosulfate-T, and thiazole orange. Further contemplation is the use of any suitable derivatives or functional equivalents thereof. The invention further contemplates the use of fluorescent agglutination staining agents that may not yet be developed but satisfy the aforementioned functions. Further information is available from suitable literature resources, such as, for example, Samuel M. Rapoport, 2019, The reticulocyte, 1 st ed., CRC Press.

[0042] Staining can be performed according to a suitable procedure known to a person skilled in the art. In some embodiments, staining is performed according to the procedure recommended by the staining reagent manufacturer. For example, a typical staining procedure may involve adding the staining reagent, for example, at a concentration of about 1%, to a mixture of whole blood sample as defined herein and an optional buffer (e.g., PBS). The mixture is then incubated for a period of time, such as 1 minute, 2 minutes, etc. The incubation time may be adjusted depending on the dye to be used. For example, for staining methods using fluorescent dyes, the incubation time may be extended by several minutes, preferably according to the manufacturer's instructions.

[0043] For subsequent analysis, the stained sample can be provided in any suitable form. For example, the sample can be analyzed in a liquid or solution, such as directly after staining. Alternatively, the sample can be stored for a period of time until analysis. For such storage, the stained sample can be covered with a coverslip, or it can be mounted with an aqueous or non-aqueous mounting medium. If a mounting medium is used, the sample can optionally be covered with a coverslip, especially if the sample needs to be stored for a long period. This method generally allows for stable preservation and near-permanent storage of the sample. Examples of suitable aqueous mounting media include Aquatex, gelatin, glycerol, Kaiser glycerol gelatin, and sorbitol F liquid E420. Suitable examples of non-aqueous mounting media include DPX, entelan rapid mounting medium, M-Glas liquid coverslips, and neo-mount anhydrous mounting medium. In another embodiment, cells can be fixed with any suitable fixation method and reagent. For example, glutaraldehyde can be used.

[0044] Additional information is available from appropriate sources, such as the Clinical and Laboratory Standards Institute document H44-A2 or any further version of "Methods for Reticulocyte Counting (Automated Blood Cell Counters, Flow Cytometry, and Supravital Dyes); Approved Guideline – Second Edition".

[0045] After staining and optional storage, the mixture can be placed on a suitable carrier for subsequent analytical steps, or it can be analyzed in situ or transferred to an analytical apparatus. Alternatively, staining can be performed in the same analytical apparatus used for subsequent analyses.

[0046] In a very specific embodiment, the staining procedure according to the invention can be performed concurrently with or prior to separate nucleic acid cross-linking. For example, such cross-linking can be performed using any suitable cross-linking reagent known to those skilled in the art. Examples of such reagents include nitrogen mustards, i.e., alkylating agents having a bis(2-ethylchloro)amine core structure with a variable R group, such as cyclophosphamide, chlorambucil, uramustine, melphalan, or bendamustine. Further examples include cis-dichlorodiamineplatinum(II), i.e., cisplatin, which is capable of forming intra- or inter-chain cross-links. Variants or derivatives of cisplatin are further contemplated. Another example is chloroethylnitrosourea (CENU), particularly carmustine (BCNU). Other cross-linking agents, such as psoralen or mitomycin C, are also contemplated. A staining step that can be performed simultaneously after the cross-linking step can also be used. In a further embodiment, the use of the cross-linking reagent can also be combined with staining using non-agglutinating dyes, such as non-agglutinating super-active dyes or non-agglutinating fluorescent dyes.

[0047] Following the staining step, the stained sample is irradiated. Irradiation is performed using a beam of light from a light detection device. The use of light in the range of 200 nm to 780 nm is envisioned. The wavelength of the light can be adapted to one or more factors, such as the nature of the dye and its excitation wavelength, the form of the light detection device, and its functional spectrum. As used herein, the term "light detection device" refers to any optical system capable of detecting and visualizing light reflected from a sample, particularly cells in the sample, such as reticulocytes. In a preferred embodiment, the light detection system is a microscope or microscopy system capable of visualizing and / or fluorescently characterizing cells. It may include a light source or be connected to a light source, which may be a laser or a light source for visual inspection. In particular, the laser may be a laser that allows stimulation of a fluorescent staining reagent, preferably a fluorescent staining reagent as described herein. Thus, the microscopy system may be a system capable of performing fluorescence microscopy examination. The microscopy system can receive visual reflections and / or fluorescent responses to stimuli from the sample, such as cells to be analyzed. The microscope may further include elements known to those skilled in the art, such as focusing optics and / or apertures, for example, designed as lenses. The microscopy system can also be connected to evaluation modules, imaging capture modules, AI modules or neural networks, computer systems, computer networks or interfaces, databases, image repositories, or laboratory or hospital systems. In certain embodiments, the microscopy system may include or be substantially built on a flow cytometer or a system incorporating flow cytometry functionality, particularly when using fluorescent dyes. Examples of flow cytometry systems contemplated by this invention include the Siemens ADVIA 2120i, Sysmex XN-series, Sysmex XE-series, Abbott DiagnosticsCell-DYN Sapphire, and Beckman Coulter HmX. The Siemens ADVIA 2120i system is particularly preferred. In further embodiments, these systems may be combined with other systems or components or units of other systems, or with separate additional components or units.

[0048] After irradiation of the stained sample, the reticular tissue or reticular network structure within reticulocytes becomes detectable. This structure can have different forms, sizes, perimeters, and optical densities, depending on the developmental stage of the reticulocytes. Typically, reticulocytes can be classified according to the following four categories (Heilmeyer): Category 1 = immature reticulocytes with dense reticular tissue, Category 2 = reticulocytes with extensive but loose reticular networks, Category 3 = reticulocytes with scattered reticular tissue networks, and Category 4 = mature reticulocytes with scattered reticular tissue granules. Figure 1 Examples are provided according to Heilmeyer's categories 1 through 4.

[0049] In a further central step of the method of the present invention, for each reticulocyte, parameters are determined that allow for a metrically unbiased decision regarding the reticulocyte's class and thereby allow for a metrically unbiased decision regarding the reticulocyte's developmental status. These parameters are the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) and the fraction (Γ) of the reticular perimeter (Ur) relative to the reticular area (Ar).

[0050] The fraction (Λ) of the reticular tissue area (Ar) relative to the total cell area (Ac) can be determined as:

[0051]

[0052] Among them, A r,i It is the area of ​​all individual stained nucleic acid regions, that is, the area of ​​all individual parts of the reticular tissue or reticular network within a reticulocyte; and where Ac is the area A measured therein. r,i And measure the area of ​​the entire reticulocyte fraction Γ.

[0053] The fraction (Γ) of the perimeter (Ur) of the reticular tissue relative to its area (Ar) can be determined as:

[0054]

[0055] Among them, U r,i It is the perimeter of all the individually stained nucleic acid regions, that is, the perimeter of all the individual parts of the reticular tissue or reticular network within a reticulocyte; and among them, A r,i It is the area of ​​all the individually stained nucleic acid regions, that is, the area in which U is measured. r,i And to measure the area of ​​all individual parts of the reticular tissue or reticular network within the reticulocytes of fraction Λ.

[0056] The obtained values ​​are stored in, for example, a computer system and / or an evaluation module and / or a database, for subsequent comparison or evaluation processing.

[0057] Typically, in the context of this invention, portions of reticular tissue or reticular networks smaller than about 200 nm may be undetectable, particularly when using microscopy techniques as described herein. Therefore, the detectability limit for reticular tissue area can be set to about 0.15 µm. 2 .

[0058] In subsequent steps, reticulocyte maturity is classified into 1 to 4 major maturity categories. This classification is performed based on the defined Λ and Γ values ​​as described above.

[0059] Advantageously, calculating the perimeter Ur of all nucleic acid fragments (especially RNA fragments) and dividing it by the reticular area Ar allows for a significant increase in the accuracy of classifying reticulocytes into categories 1 to 4 as defined above. Furthermore, using the perimeter in the formula (i.e., for maturity classification) advantageously allows for the differentiation of cells with fewer granules of larger reticular area from cells with more granules of smaller area. Thus, this novel method provides, for the first time, a suitable metric that transforms Heilmeyer's morphological observations into an automated algorithm for highly accurate reticulocyte maturity classification. Therefore, this invention provides a quantitative analytical method; however, morphological methods are qualitative and thus convey strong subjective biases, reducing their comparability.

[0060] In a particularly preferred embodiment, the maturity classification of reticulocytes is based on the Λ / Γ value used. This ratio combines the two score values ​​and allows for appropriate conversion to unique values, such as those derived from... Figure 3 This was derived from [the data]. Therefore, the maturity classification includes assigning reticulocytes to Heilmeyer categories 1, 2, 3, or 4 based on the ratio Λ / Γ.

[0061] The Λ / Γ ratio can have different values, influenced by several factors such as the light detection system used, the staining protocol used, the quality and age of the cells examined, and potential pretreatment steps. This variation can be compensated for using suitable calibration methods known to those skilled in the art. For example, calibration may include using a predetermined number of reticulocytes, standard staining conditions for analysis under different light detection systems, etc. In some embodiments, commercially available calibration solutions may be used for calibration and reference purposes. Examples of such contemplated calibration solutions are Cal-Chex, Cal-Chex A Plus, or Retic-Chex manufactured by Streck, Inc.

[0062] In a more preferred embodiment, the Λ / Γ ratio can be used to classify reticulocytes according to the following values:

[0063] A Λ / Γ ratio of approximately >2.5 indicates category 1 reticulocytes, i.e., immature reticulocytes with dense reticular tissue. Therefore, a value of approximately 2.5 constitutes the boundary between category 1 and category 2, with category 2 exhibiting a Λ / Γ ratio below approximately 2.5.

[0064] A Λ / Γ value of approximately 1 to approximately 2.5 indicates category 2 reticulocytes, i.e., reticulocytes with a broad and loose reticular network. Therefore, a value of approximately 1 constitutes the boundary value between category 2 and category 3, with category 3 showing a Λ / Γ ratio below approximately 1.

[0065] A Λ / Γ value of approximately 0.35 to approximately 1 indicates category 3 reticulocytes, i.e., reticulocytes with a disorganized reticular tissue network. Therefore, a value of approximately 0.35 constitutes the boundary value between category 3 and category 4, with category 4 exhibiting a Λ / Γ ratio below approximately 0.35.

[0066] A Λ / Γ value of approximately <0.35 indicates category 4 reticulocytes, i.e., mature reticulocytes with scattered reticular tissue granules.

[0067] Due to optical measurement tolerances (e.g., measurement tolerances of different analytical devices), staining differences, or differences in reticular or cell segmentation based on different programs or algorithms, the indicated boundary values ​​may vary slightly by a tolerance factor of ±25%, preferably ±15% or ±10%, and more preferably ±5%, ±3%, ±2%, ±1% or ±0.5%.

[0068] The present invention further envisions providing boundary classes between categories 1 and 2, 2 and 3, and 3 and 4. These boundaries may include reticulocytes that, for example, cannot be clearly classified into category 1 or 2, 2 or 3, or 3 or 4 due to the Λ / Γ values ​​corresponding to the mentioned boundary values. Boundary classes can be further established based on the tolerance factors mentioned above applied to the class definitions provided above. It is further envisioned that, for example, after calculations considering optical and chemical differences in reticulocyte staining and detection, the boundary classes can be reconnected to categories 1 through 4 with the aid of appropriate calibration factors. Further information can be obtained from suitable literature sources, such as Samuel M. Rapoport, 2019, The reticulocyte, 1 st ed., CRC Press.

[0069] The present invention further relates to a method including the step of enumerating reticulocytes. As used herein, the term "enumerating" refers to counting and summing the number of reticulocytes in each defined area, volume, time period, or other suitable unit (preferably each defined volume, such as a sample volume). In some embodiments, enumeration may be performed on each reticulocyte class as defined herein. For example, all reticulocytes in classes 1, 2, 3, and / or 4 of a sample may be counted. In a further specific embodiment, enumeration may further include counting non-reticulocytes in the sample, preferably erythrocytes. The corresponding numbers may be further compared with the reticulocyte count, the number of reticulocytes in class 1, 2, 3, or 4, and / or (e.g., previous counts from the same subject), different samples from the same subject, reference values ​​from a database, calibration references mentioned herein, reference values ​​from textbooks or other literature sources, reference values ​​from independently determined healthy or diseased subjects, etc.

[0070] In a further particularly preferred embodiment, the present invention envisions a method as defined above, wherein step (c) is performed using a device comprising an imaging module, namely determining the parameters (i) the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) for each reticulocyte and (ii) the fraction (Γ) of the perimeter (Ur) of the reticular area (Ar) relative to the reticular area (Ar).

[0071] As used herein, the term "imaging module" refers to a unit capable of performing image processing procedures. Therefore, the present invention envisions acquiring images of reticulocytes or other cellular components present in samples as described herein, preferably images of stained reticulocytes present in samples as described herein. This image acquisition may further include preprocessing or scaling activities.

[0072] Furthermore, the present invention specifically envisions image processing of the obtained images. As used herein, the term "image processing" refers to a general method of converting an image into a digital form and performing operations on it to enhance the image and / or extract useful or desired information. The output of image processing may be a modified image or features or values ​​associated with the image.

[0073] The imaging module according to the invention is designed to perform image processing, and in some embodiments, together with other modules, programs, databases, image repositories, or networks.

[0074] Image processing may include one or more of the following activities, functions, or procedures: image enhancement, including brightness or contrast adjustment; wavelet and multi-resolution processing, including image subdivision and pyramidal representation; compression activities, including techniques for reducing the storage space required to save an image or the bandwidth required to transmit an image; morphological processing, including extracting image components (image components) needed to represent or describe the shape, area, or perimeter information of cells or cellular components; segmentation, i.e., dividing an image into its components or objects; representation of data provided in the segmentation step; description, i.e., extracting attributes from segmented data, including providing quantitative data that allows one class of objects to be distinguished from another; and object recognition, i.e., assigning labels to objects based on their descriptions. These activities, functions, or procedures may preferably be performed automatically or programmatically, for example, based on the use of a computer program or AI module. Particularly preferred is that the imaging module is designed to perform morphological segmentation operations, i.e., extracting image parameters and dividing them into components or objects, such as reticular tissue structure, reticular network structure, cell perimeter, cell area, reticular tissue area, reticular tissue perimeter, staining intensity within reticular tissue, and differences in staining intensity within reticular tissue.

[0075] In a further preferred embodiment of the invention, the method includes step (e) as an additional step, comprising a morphological comparison of each stained reticulocyte with an image repository of reticulocytes that have been independently classified by experts. As used herein, the term "morphological comparison" refers to a matching and contrasting activity relating to one or more of the following characteristics associated with cells (particularly reticulocytes present in a sample, or more preferably subcellular portions, such as reticular tissue or reticular networks): structure, shape, form, size, pattern of presence, optical / visual appearance, contrast, color, or staining density. This comparison includes matching and contrasting images (preferably images already processed in the imaging module as described herein) with images or data that have been pre- or alternatively classified by experts into categories 1 to 4 according to Heilmeyer and stored in an image repository along with the classification information. These stored images may be further processed in a manner similar to or identical to that performed on the reticulocyte images obtained using the method according to the invention.

[0076] Particularly preferred is that the morphological comparison involves applying an image of stained reticulocytes to a machine learning-based method. The concept of "machine learning" as used in the context of this invention generally relies on a two-step process: a first, a training phase; and a second, a prediction phase. In the training phase, the values ​​of one or more parameters of a machine learning model (MLM) are set using training techniques and training data. In the prediction phase, the trained MLM operates on the measurement data. Example parameters of an MLM include: the weights of neurons in a given layer of an artificial neural network (ANN) such as a convolutional neural network (CNN); the kernel value of a classifier kernel, etc.

[0077] Constructing an MLM may include a training phase that determines parameter values. Particularly preferred is training using images from an image repository of reticulocytes. As mentioned above, the images in the repository are advantageously classified independently by experts. For example, the experts could be histologists or hematologists. In a further embodiment, the experts could also be a group of experts who adjust or coordinate their subjective classifications. The corresponding results, i.e., labeling the reticulocyte images as category 1, 2, 3, or 4, are then stored in the image repository along with the images, for example. The MLM can retrieve this information and use it as a training set.

[0078] Building an MLM often involves determining the values ​​of one or more hyperparameters. Typically, the values ​​of one or more hyperparameters in an MLM are set during the training phase and remain unchanged. Therefore, the values ​​of hyperparameters can be changed in the outer loop iterations, while the parameter values ​​of the MLM can be changed in the inner loop iterations. Sometimes, there can be multiple training phases so that multiple values ​​of one or more hyperparameters can be tested or even optimized. The performance and accuracy of most MLMs heavily depend on the values ​​of their hyperparameters.

[0079] Examples of hyperparameters include: the number of layers in a convolutional neural network, the kernel size of the classifier kernel, the input neurons of the ANN, the output neurons of the ANN, the number of neurons per layer, and the learning rate.

[0080] Various types and kinds of MLMs can be used in the context of this invention. For example, novel detector MLMs / anomaly detector MLMs or classifier MLMs, such as binary classifiers, can be employed. For example, deep learning (DL) MLMs can be employed: here, the features detected by the DL MLM may not be predefined, but rather set by various parameter values ​​that the model can learn during training.

[0081] Various techniques can be used to build an MLM. For example, the type of training can vary depending on the MLM type. Furthermore, the training type employed can differ across implementations. For instance, iterative optimization can be used, which employs an optimization function defined for one or more error signals.

[0082] The results of the morphological comparison operation for each stained reticulocyte are then compared with the classification results according to the Λ and Γ measurement methods of the present invention as defined above. If the two classification methods are consistent, no specific label, warning, or note is required. In some embodiments, consistency can be stored as "measurement classification confirmed by morphology," etc. If the results of the Λ and Γ measurement methods are inconsistent with the morphological method, a label is attached to the image of the reticulocytes where the difference was detected (e.g., a real-time or stored image). This label can further alert or message the operator for further analysis of the obtained results. Alternatively, the measurement and morphological analysis can be rerun to confirm the results. The label can also contain an internal classification based on the measured difference. For example, if the Heilmeyer class defined by the measurement method differs from the class defined by the morphological method by 1, e.g., the measured class = 2, the morphological class = 1, and vice versa, then an internal value of 1 (= degree of difference) is attached to the label. If the Heilmeyer class defined by the metric method differs from the class defined by the morphological method by more than 1, for example, 2 (e.g., the metric-defined class = 3, the morphological-defined class = 1), or vice versa, then an internal value of 2 (= difference degree) is assigned to the label. Further analysis and / or control operations can be performed based on the degree of difference, which may also include examining equipment, optical devices, image repositories, etc.

[0083] For diverging classification with a difference of 1, in a particular set of embodiments, the present invention envisions selecting the result obtained by the metric. For diverging classification with a difference of 2, in a particular set of embodiments, the present invention envisions selecting a classification between the metric and the morphologically defined category. For diverging classification, the present invention further envisions labeling samples containing diverging results and performing independent analysis of the samples, or performing independent analysis of other (e.g., parallel) samples from the same subject using different analytical or preparation methods (e.g., based on blood smears).

[0084] In a further aspect, the present invention relates to a computer-implemented method for classifying reticulocyte maturity based on one or more images captured from a whole blood sample as defined above. The method includes determining, within the image, parameters (i) the fraction (Λ) of the reticular area (Ar) relative to the total cell area (Ac) for each reticulocyte and (ii) the fraction (Γ) of the reticular perimeter (Ur) relative to the reticular area (Ar); and classifying the reticulocyte maturity into four main categories based on the Λ and Γ values. In some embodiments, the method further includes an enumeration step of reticulocytes, preferably as defined above. The method includes image acquisition and image processing steps as defined above. In some embodiments, a morphological comparison of the images with an image repository of reticulocytes as defined above can also be implemented and performed. The method can be implemented on any suitable storage or computer platform, such as a cloud-based, internet-based, intranet-based, or platform existing on a local computer or mobile phone.

[0085] In a further aspect, the present invention relates to a data processing apparatus including means for performing a computer-implemented method as defined above. The apparatus includes means for performing any one or more steps of the computer-implemented method of the present invention as described above. Therefore, any computer-implemented method described herein can be performed wholly or partially using a computer system comprising one or more processors configured to perform the steps. Thus, some embodiments of the invention relate to a computer system configured to perform steps of any computer-implemented method described herein, potentially having different components for performing individual steps or groups of steps. The respective steps of the method can further be performed simultaneously or in different orders. Furthermore, a portion of these steps can be used in conjunction with a portion of other steps from other methods. Additionally, all or part of the steps can be optional. Furthermore, any step of any of the said methods can be performed using modules, circuitry, or other means for performing these steps.

[0086] A computer program is also envisioned that includes instructions that, when executed by a computer, cause the computer to perform any computer-implemented method of the present invention as defined herein, or any one or more computerizable steps of the method of the present invention as described herein.

[0087] It is also envisioned to provide a computer-readable storage medium containing a computer program product as defined above. The computer-readable storage medium may be connected to server components, reside in a cloud infrastructure, or be connected to one or more database structures or client databases via the Internet or intranet.

[0088] For example, using conventional or object-oriented techniques, and any suitable computer language, such as Java, Python, Javascript, VB.Net, C++, C#, C, Swift, Rust, Objective-C, Ruby, PHP, or Perl, any software component or computer program or function described herein can be implemented as software code executed by a processor. The software code can be stored as a series of instructions or commands on a computer-readable medium for storage and / or transmission. Suitable media include random access memory (RAM), read-only memory (ROM), magnetic media such as hard disk drives, or optical media such as optical discs (CDs) or DVDs (Digital Versatile Discs), flash memory, etc. The computer-readable medium can be any combination of such storage or transmission devices. Such programs can also be encoded and transmitted using carrier signals suitable for transmission over wired, optical, and / or wireless networks (including the Internet) conforming to various protocols. Therefore, a computer-readable medium according to the invention can be created using data signals encoded with such a program. Computer-readable media encoded with program code can be packaged with compatible devices or provided separately from other devices (e.g., downloaded via the Internet). Any such computer-readable medium may reside on or within a single computer program product (such as a hard disk drive, CD, or an entire computer system) and may reside on or within different computer program products within a system or network. A computer system may include a monitor, printer, or other suitable display for providing a user with any of the results mentioned herein.

[0089] In a further aspect, the present invention relates to an in vitro method for monitoring and determining the health status of a subject and / or the subject's response to treatment. The method includes performing a maturity classification of reticulocytes from a whole blood sample from a subject, as defined herein. Particularly preferred is that the method includes the step of enumerating reticulocytes over the entire sample and / or by categories as defined herein. After classifying and optionally enumerating the reticulocytes in the sample, the obtained results can be compared with one or more reference values ​​or numbers. For example, the number of reticulocytes per sample volume or per category per sample volume can be compared with numbers obtained from normal or healthy subjects. Furthermore, they can be compared with numbers obtained from subjects diagnosed with a specific disease, such as diseases that typically affect erythropoiesis, blood cell circulation, or blood cell count, such as anemia or bone marrow disorders. In a further embodiment, these numbers can be compared with reference numbers from databases, textbooks, literature sources, hospital documents, etc.

[0090] As used herein, the term "health status" refers to the presence or absence of disease in the subject, such as compared to a healthy individual. In some implementations, the term may further refer to trends in health development, such as the deterioration or improvement of a medical condition or disease condition.

[0091] In some embodiments, the method for monitoring or determining the health status of a subject may provide the total number of reticulocytes in a sample as a result, which is lower than the number of reticulocytes in a reference sample of a healthy subject; or it may provide the number of reticulocytes in category 1 as a result, which is lower than the number of reticulocytes in the same category 1 in the reference sample of a healthy subject, while other categories show similar numbers in the examination sample and the reference sample; or it may provide the number of reticulocytes in category 2 as a result, which is lower than the number of reticulocytes in the same category 2 in the reference sample of a healthy subject, while other categories show similar numbers in the examination sample and the reference sample; or it may provide the number of reticulocytes in category 3 as a result, which... A reticulocyte count lower than that in the reference sample of a healthy subject in the same category 3, while other categories show similar numbers in the test sample and reference sample; or it provides the reticulocyte count in category 4 as a result, which is lower than that in the reference sample of a healthy subject in the same category 4, while other categories show similar numbers in the test sample and reference sample; or it provides the reticulocyte counts in categories 1 and 2, or categories 2 and 3, or categories 3 and 4, or categories 1, 2 and 3, or categories 2, 3 and 4 as a result, which are lower than the reticulocyte count in the corresponding category in the reference sample of a healthy subject, while other categories show similar numbers in the test sample and reference sample. These results can be considered indicative of bone marrow failure, such as due to drugs, tumors, radiation therapy, or infection; cirrhosis; anemia, possibly due to low iron levels or low vitamin B12 or folic acid levels; or chronic kidney disease. In the context of this invention, these diseases are understood as "hyporeticulocyte diseases".

[0092] In some embodiments, the method for monitoring or determining the health status of a subject may provide the total number of reticulocytes in a sample as a result, which is higher than the number of reticulocytes in a reference sample of a healthy subject; or it may provide the number of reticulocytes in category 1 as a result, which is higher than the number of reticulocytes in the same category 1 in the reference sample of a healthy subject, while other categories show similar numbers in the examination sample and the reference sample; or it may provide the number of reticulocytes in category 2 as a result, which is higher than the number of reticulocytes in the same category 2 in the reference sample of a healthy subject, while other categories show similar numbers in the examination sample and the reference sample; or it may provide the number of reticulocytes in category 3 as a result, which... A higher reticulocyte count than the same category 3 reticulocyte count in the reference sample from a healthy subject, while other categories show similar numbers in the test sample and reference sample; or a higher reticulocyte count in category 4 than the same category 4 reticulocyte count in the reference sample from a healthy subject, while other categories show similar numbers in the test sample and reference sample; or a higher reticulocyte count in categories 1 and 2, or categories 2 and 3, or categories 3 and 4, or categories 1, 2 and 3, or categories 2, 3 and 4 than the corresponding category reticulocyte count in the reference sample from a healthy subject, while other categories show similar numbers in the test sample and reference sample. These results can be considered indicative of anemia, possibly due to premature destruction of red blood cells (i.e., hemolytic anemia); bleeding; a blood disorder of the fetus or newborn (erythropoietinosis); or kidney disease with increased production of erythropoietin. In the context of this invention, these diseases are understood as "high reticulocyte disease".

[0093] As used herein, “response to treatment” refers to a subject’s positive or negative response to treatment for a disease that may affect erythropoiesis or be associated with any of the aforementioned diseases or with any further diseases diagnosed in the same disease.

[0094] In some embodiments, the contemplated methods for monitoring and determining a subject's health status and / or response to treatment include using more than one sample obtained from the subject in accordance with the methods of the present invention. For example, samples may be collected at an initial time point, followed by further samples collected after a certain time period. The time period can be any period deemed suitable by those skilled in the art. The time period can be controlled by the subject's illness or treatment, their hospitalization status, the subject's health status, or any other diagnostically relevant factors. In some embodiments, further samples are collected from the subject after a time period of 2, 3, 4, 5, 6, 7 days, or more. Time periods of 10 days, 14 days, 3 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, or longer are also contemplated.

[0095] Comparison of enumeration and classification procedures using samples collected from subjects at different times can provide results such as: similar numbers and / or class distributions of reticulocytes, or an overall increase in the number of reticulocytes or within one or more classes, or an overall decrease in the number of reticulocytes or within one or more classes. These results can then be used to attribute diagnostic conclusions.

[0096] For example, in cases of low reticulocyte counts, such as bone marrow failure (e.g., due to medication, tumors, radiation therapy, or infection), cirrhosis, anemia (possibly due to low iron or low vitamin B12 or folic acid levels), or chronic kidney disease, an overall increase in reticulocyte count or in category 2, 3, or 4 indicates an improvement in the subject's health and / or a positive response to treatment when comparing initial and subsequently collected samples.

[0097] Similarly, in cases of high reticulocyte counts, such as hemolytic anemia, bleeding, fetal or neonatal blood disorders (erythropoietinemia), or kidney disease with increased erythropoietin production, an overall increase in reticulocyte count or category 2, 3, or 4 indicates a deterioration in the subject's health and / or a negative response to treatment when comparing initial and subsequently collected samples.

[0098] In a further embodiment, in cases of high reticulocyte counts, such as hemolytic anemia, bleeding, fetal or neonatal blood disorders (erythropoietinemia), or kidney disease with increased erythropoietin production, a decrease in overall or category 2, 3, or 4 reticulocyte counts when comparing initial and subsequently collected samples indicates an improvement in the subject's health and / or a positive response to treatment.

[0099] In another embodiment, in cases of low reticulocyte counts, such as bone marrow failure (e.g., due to drugs, tumors, radiation therapy, or infection), cirrhosis, anemia (possibly due to low iron or low vitamin B12 or folic acid levels), or chronic kidney disease, a decrease in overall or category 2, 3, or 4 reticulocyte counts when comparing initial and subsequently collected samples indicates a deterioration in the subject's health and / or a negative response to treatment.

[0100] In a further aspect, the present invention relates to an apparatus comprising devices for performing the method according to the invention. Thus, the apparatus may include modules capable of performing staining activities as defined above. The staining module may include elements for cell preparation, cell sorting, cell purification, chemical treatment, staining, washing, and optionally storage functions. For example, the module may consist of a robotic entity or function. The apparatus may further include a module capable of irradiating the stained sample. This module may be in the form of a microscope or microsystem and may include focusing optics that can be designed as lenses and / or apertures. In a specific embodiment, the microsystem may additionally include or substantially build upon a flow cytometer or a system incorporating flow cytometry functionality, particularly when using fluorescent dyes. The microscope module may be further connected to an evaluation module capable of determining the intracellular parameters Λ and Γ of reticulocytes as described herein. This evaluation module may, for example, be implemented as or connected to an imaging module as defined above, i.e., a unit capable of performing image processing procedures using images of reticulocytes or other cellular components present in the sample as described herein (preferably images of stained reticulocytes present in the sample as described herein). A maturity classification module is also envisioned, capable of attributing measured reticulocyte parameters Λ and Γ values ​​to categories 1 through 4 as described by Heilmeyer. For example, this module could be a computer-based module, an AI module, a neural network, a computer system, a computer network, or an interface, and could optionally be connected to a database, image repository, or laboratory or hospital system.

[0101] Example

[0102] Example 1

[0103] Reticulocyte detection

[0104] Materials and methods for reticulocyte detection

[0105] Samples. With informed consent and after consultation with the University of Erlangen Ethics Committee ( Ethikkommission der Universität Erlangen Peripheral blood was drawn from healthy donors under the procedure approved by application 316_14B. Blood from each sample was collected in a 4.7 ml EDTA-coated tube. All samples were processed within 6 hours of collection.

[0106] Immature erythrocytes were identified using a highly bioactive dye that precipitates cytoplasmic RNA into a reticular, organized network. We used basic neomethylene blue (NMB, c = 0.5%). Sigma- Aldrich Dye treatment of reticulocytes. The dye solution is added to whole blood for 1 minute. The resulting blood smear allows visualization of this filamentous network under an optical microscope.

[0107] Optical configuration. A camera (Apex 3-CMOS prism-based RGB camera, JAI) mounted on a DMi8 Leica inverted microscope was used, through a 40x objective lens (HP PL APO 40x / 0.95 CORR PH2). Leica Bright-field images are recorded in transmission mode to enable the identification of reticulocytes.

[0108] Statistical Analysis. Image analysis was performed using ImageJ. The stained reticular formation was segmented by converting the color image to RGB-grayscale. An automatic thresholding algorithm was applied to the green channel to determine the area and perimeter of the reticular formation.

[0109] The accompanying drawings are provided for illustrative purposes. Figure 1 , Figure 2 , Figure 3 Therefore, it should be understood that these figures should not be interpreted as limiting. Those skilled in the art will clearly be able to conceive of further modifications to the principles set forth herein.

Claims

1. A method for classifying the maturity of reticulocytes from whole blood samples, comprising: (a) The sample is stained using an ultra-in vivo agglutination staining reagent or a fluorescent agglutination dye; (b) In a light detection device, a light beam is used to irradiate a stained sample to detect reticulocytes; (c) Determine the parameters for each reticulocyte: (i) the fraction Λ of the reticular area Ar relative to the total cell area Ac and (ii) the fraction Γ of the reticular perimeter Ur relative to the reticular area Ar; and (d) Based on the determined Λ and Γ values, the maturity of reticulocytes is classified into one of the four main maturity categories.

2. The method of claim 1, further comprising the step of enumerating the reticulocytes as a final step.

3. The method of claim 1 or 2, wherein, The staining was performed using a super-living agglutination staining reagent selected from NMB (new methylene blue), Brilliant cresol blue, crystal violet, methyl violet and Nile blue.

4. The method of claim 1 or 2, wherein, The staining was performed using a fluorescent condensing dye selected from acridine orange, auramine O, D-methyloxacarbonyl cyanine, ethidium bromide, pyronin Y, thiosulfate-T, and thiazole orange.

5. The method of claim 1 or 2, wherein, Step (c) is performed using a device including an imaging module.

6. The method of claim 1 or 2, wherein, The method further includes the following step as step (e): morphologically comparing each stained reticulocyte with an image repository of reticulocytes independently classified by experts.

7. The method of claim 6, wherein, The morphological comparison includes applying images of the stained reticulocytes to a machine learning-based method, which is trained using images from a reticulocyte image repository.

8. The method of claim 1 or 2, wherein, The maturity classification includes assigning reticulocytes to category 1, 2, 3, or 4 based on the ratio Λ / Γ.

9. The method according to claim 8, wherein, A Λ / Γ value greater than 2.5 indicates category 1, a Λ / Γ value between 1 and 2.5 indicates category 2, a Λ / Γ value greater than or equal to about 0.35 and less than about 1 indicates category 3, and a Λ / Γ value less than 0.35 indicates category 4, where "about" indicates a deviation of ±25% from the indicated value.

10. The method according to claim 1, wherein, The optical detection device is a microscope.

11. The method according to claim 1, wherein, The light beam has a wavelength range of 200 nm to 780 nm.

12. The method according to claim 2, wherein, The enumeration is performed by category and by sample.

13. The method according to claim 3, wherein, The staining further includes a chemical nucleic acid cross-linking step.

14. The method according to claim 13, wherein, The chemical nucleic acid cross-linking is performed using nitrogen mustard, cis-dichlorodiamine platinum(II) or its derivatives, or chloroethylnitrosourea (CENU).

15. The method according to claim 4, wherein, The staining further includes a chemical nucleic acid cross-linking step.

16. The method according to claim 15, wherein, The chemical nucleic acid cross-linking is performed using nitrogen mustard, cis-dichlorodiamine platinum(II) or its derivatives, or chloroethylnitrosourea (CENU).

17. The method according to claim 5, wherein, The imaging module is designed to perform morphological segmentation operations.

18. The method according to claim 6, wherein, The morphological comparison results in the labeling of reticulocytes whose morphological classification differs from that of step (d).

19. A computer-implemented method for classifying the maturity of reticulocytes based on one or more images taken from a whole blood sample, comprising determining within the image parameters for each reticulocyte: (i) a fraction Λ of the reticular area Ar relative to the total cell area Ac and (ii) a fraction Γ of the reticular perimeter Ur relative to the reticular area Ar; and classifying the maturity of the reticulocytes into four main categories based on the values ​​of Λ and Γ.

20. The method according to claim 19, wherein, The method further includes the step of enumerating the reticulocytes.

21. An apparatus comprising means for performing the method of any one of claims 1 to 18.

22. A data processing apparatus including means for performing the method of claim 19 or 20.

23. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method of claim 6, 7, 18, 19, or 20.

Images