Campus Monterrey
School of Engineering and Sciences
Analysis of immune cells in peripheral blood and colostrum from obese mothers in Mexico
A thesis presented by
Piñeiro-Salvador Raúl
Submitted to the
School of Engineering and Sciences
in partial fulfillment of the requirements for the degree of
Master of Science In Biotechnology
Monterrey Nuevo León, May 28th 2021
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I dedicate this thesis to PhD. María Dolores Montalvo Parra (Lola), who inspired me to becoming a scientist and guided me through this incredible process.
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I would like to acknowledge all people who supported me during this project and stage of my personal development, and those who took part in any of the stages of it.
Special thanks to all of the volunteer mothers, who donated part of themselves so this project could be possible.
To all the doctors involved in sampling. Doctors Dalia, Alan and Gelacio, doctors Eduardo and Jorge, as well as Dr. Mario Alcorta
To all the members of my committee. Dr. Victor Lara, Dr. Eduardo Vázquez, Dr. Cuauhtémoc Licona and Dr. Marion Brunck.
Thanks to Tecnológico de Monterrey for the scholarship and facilities provided. And to Consejo Nacional de Ciencia y Tecnología (CONACYT) for the economical support.
To all members of Engineered Cell Therapies, and Industrial Genomics labs for their help.
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by
Piñeiro-Salvador Raúl
Abstract
Breastfeeding provides newborns not only nutritional short-term nutrient supply, but also long- term benefits in neural, metabolic, and immune development. Several bioactive components of human milk mediate these long-term benefits over immunity, including living bacteria and leukocytes, or even some modulators like cytokines and antibodies. The immunological properties of milk change over lactation progress and the colostrum (stage of lactation produced within 2 days after birth) is the stage with the highest concentration of these factors. Other factors like maternal health status, genetics or daily variations also influence on milk composition. In this scenario, prolactin promotes leukocyte migration to mammary glands and milk. However, the role of chronic conditions on human milk immune composition like obesity is poorly studied. This is especially important in countries with high rates of maternal obesity, like Mexico, where more than two-thirds of adult women suffer any grade of overweight or obesity. Here, we present an extensive flow-cytometry based characterization of leukocyte subpopulations in peripheral blood and colostrum from lean and obese mothers, as well as an analysis of some of their phenotypes.
With this data, we aim to better understanding if obesity affects the process of leukocyte migration to human milk, and promotes possible changes on functionality of human milk leukocytes.
Keywords: Breastfeeding – Leukocytes – Mexico – Obesity – Flow cytometry
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Figure 1. Separation index (SI) graphs for each antibody-fluorochrome. Blue lines correspond to PBMCs and red lines correspond to granulocytes. a) CD2 - APC; b) CD16 - APC-H7; c) CD19 - V450; d) CD36 - PE; e) CD45 - V500; f) CD294 - Alexa Fluor 647. ...18 Figure 2. Ancestry-type analysis of the full antibody panel in peripheral blood. Panels 1 to 13 were successively analyzed. Panel 1: cells gating and exclusion of debris. Panel 2: single cells gating.
Panel 3: live cells gating. Panel 4: gating of leukocytes (CD45+ cells). Panel 5: gating of neutrophils and gates B, C and D. Panel 6: gating of non-classical (CD16+) monocytes, and cytotoxic T cells and NK. Panel 7: creation of gates E and F. Panel 8: gating of immature granulocytes and creation of gate I. Panel 9: gating of eosinophils. Panel 10: gating of basophils and non-cytotoxic T cells. Panel 11: creation of gates G (CD36+/CD19-) and H (CD19+/CD36-).
Panel 12: gating of classical (CD16-) monocytes. Panel 13: gating of B cells ...21 Figure 3. Ancestry-type analysis of the full antibody panel in colostrum-enriched cells. Panels 1 to 13 were successively analyzed. Panel 1: cells gating and exclusion of debris. Panel 2: single cells gating. Panel 3: live cells gating. Panel 4: gating of leukocytes (CD45+ cells). Panel 5: gating of neutrophils and gates B, C and D. Panel 6: gating of non-classical (CD16+) monocytes, and cytotoxic T cells and NK. Panel 7: creation of gates E and F. Panel 8: gating of immature granulocytes and creation of gate I. Panel 9: gating of eosinophils. Panel 10: gating of basophils and non-cytotoxic T cells. Panel 11: creation of gates G (CD36+/CD19-) and H (CD19+/CD36-).
Panel 12: gating of classical (CD16-) monocytes. Panel 13: gating of B cells ...23 Figure 4. Overlapping of peripheral blood (red) and colostrum (blue) successive panels used on gating strategy. ...25 Figure 5. Relative frequency of immature granulocytes. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples.
...28 Figure 6. Relative frequency of eosinophils. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples ...30 Figure 7. Relative frequency of basophils. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples ...32 Figure 8. Relative frequency of neutrophils. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples ...33 Figure 9. Relative frequency of B cells. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples ...35
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...37 Figure 11. Relative frequency of non-cytotoxic T cells. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples 39 Figure 12. Relative frequency of non-classical (CD16+) monocytes. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples ...40 Figure 13. Mean with SD for FSC-A in all leukocyte subpopulations for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...42 Figure 14. Mean with SD for SSC-A in all leukocyte subpopulations for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...44 Figure 15. Mean with SD for CD2/CD294 in cytotoxic T cells and NK, basophils, non-cytotoxic T cells and eosinophils, for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...45 Figure 16. Mean with SD for CD16 in non-classical monocytes, cytotoxic T cells and NK, and neutrophils, for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...47 Figure 17. Mean with SD for CD19 in B cells for peripheral blood (red) and colostrum (blue).
Statistically significant differences are marked with asterisks. ...48 Figure 18. Mean with SD for CD36 in non-classical monocytes for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...49 Figure 19. Mean with SD for CD45 in all leukocyte subpopulations for peripheral blood (red) and colostrum (blue). Statistically significant differences are marked with asterisks. ...51
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Table 1. Main characteristics of the study groups included on this project. ...10
Table 2. Antibody summary and best volume for staining one million cells. ...19
Table 3. Clinical characteristics of donor mothers. ...26
Table 4. Mean with SD for received volumes of samples, obtained millions of cells per milliliter of colostrum processed, percentage of live cells and percentage of CD45+ events in colostrum. .27 Table 5. Descriptive statistics for immature granulocytes. ...28
Table 6. Descriptive statistics for total immature granulocytes per milliliter of colostrum ...29
Table 7. Descriptive statistics for eosinophils. ...30
Table 8. Descriptive statistics for total eosinophils per milliliter of colostrum ...31
Table 9. Descriptive statistics for basophils. ...32
Table 10. Descriptive statistics for total basophils per milliliter of colostrum ...32
Table 11. Descriptive statistics for neutrophils...34
Table 12. Descriptive statistics of total neutrophils per milliliter of colostrum ...34
Table 13. Descriptive statistics for B cells. ...35
Table 14. Descriptive statistics for total B cells per milliliter of colostrum ...36
Table 15. Descriptive statistics for cytotoxic T cells and NK. ...37
Table 16. Descriptive statistics for total cytotoxic T cells and NK per milliliter in colostrum ...38
Table 17. Descriptive statistics for non-cytotoxic T cells. ...39
Table 18. Descriptive statistics for total non-cytotoxic T cells per milliliter of colostrum ...40
Table 19. Descriptive statistics for non-classical monocytes ...41
Table 20. Descriptive statistics for total non-classical (CD16+) monocytes per milliliter of colostrum ...41
Table 21. Mean values with SD for FSC-A in all leukocyte subpopulations ...43
Table 22. Mean values with SD for SSC-A in all leukocyte subpopulations. ...44
Table 23. Mean values with SD for CD2 and CD294 in cytotoxic T cells and NK, basophils, non- cytotoxic T cells and eosinophils. ...46
Table 24. Mean values with SD for CD16 in non-classical monocytes, cytotoxic T cells and NK, and neutrophils. ...48
Table 25. Mean values with SD for CD19 in B cells ...49
Table 26. Mean values with SD for CD36 in classical and non-classical monocytes. ...50
Table 27. Mean values with SD for CD45 in all leukocyte subpopulations for peripheral blood and colostrum in each group. ...52
ix Abbreviation Definition
µL Microliter
AF-647 Alexa Fluor 647
APC Allophycocyanin
APC-H7 Allophycocyanin H7 conjugated
BMI Body Mass Index
CBA Cytometry Bead Array
CD16 FcγRIII
CD2 T-cell surface antigen T11/Leu-5, LFA-2, or LFA-3 receptor CD294 Prostaglandin D2 receptor 2 (PTGDR2)
CD36 Fatty acid translocase (FAT), or scavenger receptor class B member 3 (SCARB3)
CD45 Protein tyrosine phosphatase receptor type C (PTPRC) CRP C-reactive protein
FBS Fetal bovine serum FSC-A Forward scattering area FSC-H Forward scattering height GALT Gut-associated lymphoid tissue hBSCs Human breast milk stem cells hESCs Human embryonic stem cells
HLA-DR Human Leukocyte Antigen – DR isotype
IL Interleukin
IL2-2 Interleukin-2 receptor
K2EDTA Dipotassium ethylenediaminetetraacetic acid
kg kilogram
m Square meters
MFI Median fluorescence intensity
mL Milliliter
NK Natural Killer
PBMCs Peripheral blood mononuclear cells PBS Phosphate buffer saline
PE Phycoerythrin
pg Picogram
PI Propidium iodide
SD Standard deviation
SI Separation index
SSC-A Side scattering area TNF-α Tumor necrosis factor α
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Abstract... v
List of Figures ... vi
List of Tables ... viii
List of abbreviations ... ix
1. Breastfeeding ...1
1.1 Human milk stages ...1
1.2 Human milk composition ...1
1.3 Probiotic bacteria ...2
1.4 Human cells in breast milk ...2
1.4.1 Origin of human cells in milk ...3
1.4.2 Non-hematopoietic cells ...3
1.4.3 Hematopoietic cells ...4
1.4.4 Functions of transferred cells ...4
1.5 Milk composition in acute and chronic conditions ...4
1.5.1 Maternal overweight and obesity ...5
1.5.2 Consequences of maternal obesity...6
1.6 Thesis outline ...7
1.6.1 Justification ...7
1.6.2 Hypothesis ...7
1.6.3 General objective ...8
1.6.4 Specific objectives ...8
2. Materials and methods ...9
2.1 Brief introduction to flow cytometry...9
2.2 Antibody titration ...9
2.3 Study groups ...10
2.4 Sampling, storage, and transport ...10
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2.6 Colostrum-enriched cells staining ...11
2.7 Peripheral blood staining ...12
2.8 Sample acquisition ...13
2.9 Gating strategy ...13
2.10 Data analysis and statistics ...15
3. Results ...17
3.1 Development of protocols ...17
3.1.1 Antibody titration ...17
3.1.2 Test of complete flow cytometry panel ...19
3.2 Analysis of clinical samples ...26
3.2.1 Immature granulocytes ...27
3.2.2 Eosinophils ...29
3.2.3 Basophils...31
3.2.4 Neutrophils ...33
3.2.5 B cells ...34
3.2.6 Cytotoxic T cells and NK ...36
3.2.7 Non-cytotoxic T cells ...38
3.2.8 Non-classical (CD16+) monocytes ...40
3.2.9 FSC-A ...42
3.2.10 SSC-A ...43
3.2.11 CD2 and CD294 ...45
3.2.12 CD16 ...46
3.2.13 CD19 ...48
3.2.14 CD36 ...49
3.2.15 CD45 ...50
4. Discussion ...53
xii
Future perspectives ...61
7. Bibliography ...62
Appendix A: Ethics protocol approval ...73
Appendix B. Frequencies of leukocyte subpopulations in peripheral blood and colostrum...74
Appendix C: Cell enrichment ...77
Appendix D: Normality tests ...79
Appendix E. Absolute counts of colostrum cells ...80
1. Breastfeeding
Breastfeeding has several benefits for newborns. It contains all the energy and nutrients needed during early life, and reduce the overweight, obesity and diabetes incidences, while promotes early brain development. Breastfeeding also reduces the prevalence of respiratory and digestive diseases in newborns (World Health Organization, 2020). Breast milk is a continuous source of IgA, which reduces the incidence of enteric diseases (Van de Perre, 2003).
In high-income countries, breastfeeding is a common practice for 78.8% of newborns, while for middle and low-income countries this rate is 95.6% and 97.6%, respectively (UNICEF, 2018) Exclusive breastfeeding during the first 0 to 5 months of life is a common practice in 35% of children in North America, with a mean of 44% worldwide (UNICEF, 2020). In Mexico, the mean breastfeeding period is 9.8 months (INEGI, 2018).
1.1 Human milk stages
Human milk has three different stages. The colostrum is the first stage, produced during the first days postpartum. It is rich in immunological components such as immunoglobulins, lactoferrin, leukocytes, and growth factors, with lower levels of lactose, potassium, and calcium, and higher in sodium, chlorine, and magnesium than in further stages. The second stage is transition milk.
This stage usually takes place a few days after birth, when the secretory activation happens, so the volume of milk production rises. This stage is highly variable between women, and the main markers of this stage are the lactose increase, and the decrease of sodium to potassium ratio.
After two weeks after birth, milk exhibits a more stable composition.
This final stage of lactation, known as mature milk, takes place from 2 or 3 weeks post-partum until to the end of lactation. Mature milk has low levels of immunological factors, while protein and lactose contents are higher than previous stages. (Ballard & Morrow, 2013).
1.2 Human milk composition
There are two major categories of milk components: nutritional factors, and bioactive factors.
Nutritional components comprise macronutrients (proteins, sugars, and fats), and micronutrients such as vitamins and minerals. Bioactive components modify biological substrates or moderate processes in the body, with an impact on living functions. The epithelium of mammary glands produces some of them, or cells present in milk do. Some of these molecules come directly from maternal serum. The bioactive factors in human milk include bacteria, leukocytes, lactocytes,
stem and myoepithelial cells, immunoglobulins, chemokines, cytokines, growth factors, and hormones (Ballard & Morrow, 2013).
1.3 Probiotic bacteria
Initial characterization of human milk microbiota with culture-dependent techniques only let the identification of aerobic or facultative anaerobic microorganisms, like Staphylococcus, Streptococcus, and Propionibacterium. Using novel techniques like pyrosequencing, strains of anaerobic genus as Bacteroides was identified for the first time (Jost et al., 2013). Metagenomics confirmed the presence of DNA from many species and genus (Mira & Rodríguez, 2017).
There are multiple explanations for the origin of these bacteria, such as the infant’s mouth, the mother’s skin, or the use of non-sterile pumps. The most accepted theory is the entero-mammary pathway, where microorganisms in the mother’s gut are transported to the milk by dendritic cells and CD18+ cells (Ninkina et al., 2019). Some of these bacteria are part of the skin microbiome, and they might prevent the colonization by pathogens (Witkowska-Zimny & Kaminska-El-Hassan, 2017).
The precise mechanisms of development of the milk microbiota are still unknown. These microorganisms lead to the establishment of the initial microbiome in newborns’ gut, next to other microorganisms acquired during the birth. Breastfed children tend to develop a healthier and more balanced microbiome, with reduced respiratory and gastrointestinal infections on the first seven to twelve months of age, compared to those fed with milk formula. Inadequate gut colonization may lead to a dysbiosis, with further alterations on early childhood metabolism and immune system’s development (Toscano et al., 2017).
1.4 Human cells in breast milk
Smith & Goldman (1968) described the morphology of human breast milk cells using cell staining and visual count. They reported macrophages, neutrophils, lymphocytes and monocytes similar to those in peripheral blood, and epithelial cells. The mean concentration reported for macrophages was 2100 cells/mL (range from 500 to 3000), while for lymphocytes it was 205 cells/mL (range from 80 to 255), with no differences between breastfeeding and non- breastfeeding women. Finally, neutrophils varied from 700 cells/mL (range from 800-9000) in non-
breastfeeding women, compared to 150 cells/ml (range from 0 to 300) among those breastfeeding (Smith & Goldman, 1968).
1.4.1 Origin of human cells in milk
Hormones like progesterone, estrogen and prolactin promote the presence of gut-associated lymphoid tissue (GALT) IgA secretory plasma cells in mammary glands in mice. This process leads to the presence of intraepithelial IgA. However, IgG and IgM-producer plasma cells do not respond to this hormonal stimulus (Weisz-Carrington et al., 1978).
Prolactin enhances leukocyte migration to mammary glands through the increase on the expression of chemokines (mainly IL-8, CCL2, and CXCL1) by mammary gland epithelium. This hormone has a direct effect increasing CD8+, CD4+, and CD19+ lymphocytes in surrounding lymph nodes. In vitro, prolactin also increases migration of macrophages, monocytes, eosinophils and neutrophils towards stimulated areas (Dill & Walker, 2017). Interlekukin-8 is involved in neutrophil migration to mammary glands during mastitis, but its presence is normal in human milk.
Around 80% of cells in early milk are macrophages originated from monocytes on peripheral blood that migrate to the milk through the mammary epithelium. These monocytes can differentiate into dendritic cells that can stimulate T cell activity (Ballard & Morrow, 2013).
One study with bovine polymorphonuclear cells showed that, in mammary gland diapedesis, the migration trough extracellular matrix induces more apoptosis than migration trough epithelial or endothelial cells, with 69% of cells undergoing apoptosis. Migration trough endothelium induces apoptosis in about 50% of cells, while trough epithelium this rate is less than 10% (Van Oostveldt et al., 2002).
1.4.2 Non-hematopoietic cells
Flow cytometry-based analysis on colostrum evidenced the presence of different cell subsets in milk, such mesenchymal stem cells (CD90+, CD105+, CD73+) and myoepithelial cells (CD29+, CD44+) (Indumathi et al., 2013)
Human milk also contains lineages that express stem cell markers such as cytokeratin 5, α-6 integrin, and epithelial progenitor marker p63. Other milk-derived stem cells are human embryonic stem cells (hESCs) and mesenchymal stem cells (Ninkina et al., 2019).
There are pluripotent stem cells in human milk, better known as human breast milk stem cells (hBSCs), which can differentiate in vitro into adipocytes, chondrocytes, osteoblasts, neural cells, pancreatic β-cells, and even lactocytes and myoepithelial cells. These cells are active, mobile and
interact with the surrounding medium (Witkowska-Zimny & Kaminska-El-Hassan, 2017). The role of non-immune cells, such as lactocytes, myoepithelial cells, and stem cells is still unknown.
1.4.3 Hematopoietic cells
Flow cytometry showed that leukocytes in milk comprise between 13 and 70% of total cells in colostrum, while in transition and mature milk these range drops to 0 to 2%, in healthy (disease- free mothers). These leukocytes might provide active immunity to the newborns, and at the same time promote the development of immunocompetency, but also protect the mammary glands against infections. (Witkowska-Zimny & Kaminska-El-Hassan, 2017) Breast milk contains up to 2.6% of hematopoietic stem cells, without a relationship between days postpartum and cellular concentration.(Fan et al., 2010).
1.4.4 Functions of transferred cells
Breast milk cell transfer may occur because the gap junctions in epithelial cells of the gut are loose, and enzymatic activity is limited after birth (Molès et al., 2018), and the stomach is not as acid due to the presence of amniotic fluid (Mikami et al., 2019).
Studies in mice show that milk cells can be transferred to suckling pups. More than 80% survive the stomach digestion, and some of these cells, mainly CD4+ and CD8+ cells attach to the gut.
Other cells translocate from the gut and temporary migrate to organs such as thymus and spleen (Ma et al., 2008). Maternal cells are present in the brain of breastfed pups, where they differentiate into neuronal and glial cells, in a microchimerism process (Aydın et al., 2018). The long-term consequence of this maternal transfer is yet unknown.
Moreover, breastfeeding could also help to the priming of the immune system in order to develop tolerance to food antigens (Calder et al., 2006).
1.5 Milk composition in acute and chronic conditions
Many maternal, infant and physiological factors influence breast milk composition. Some of them are daily variations, lactation stage and time point in breastfeeding (hindmilk versus foremilk), whereas others like maternal nutrition and body composition are not so well characterized (Bzikowska-Jura et al., 2018). Few studies have focused on the effects of the nutritional status of women over the immunologic composition of their milk (Institute of Medicine, 1991).
The proportions of leukocytes in human milk can rise up to 93.6% during an acute maternal infection (Hassiotou et al., 2013). Infection by Zika during pregnancy, compared to mothers who were not infected, leads to lower content of IL-10 (means of 1 and 4 pg/mL, respectively), and higher content of IL-6 in milk (means of 200 and <50 pg/mL, respectively), even when there was not any infective process during breastfeeding (de Quental et al., 2019).
1.5.1 Maternal overweight and obesity
Overweight and obesity are conditions with abnormal or excessive fat accumulation that may impair health. The body mass index (BMI), which is a simple index of weight-for-height (kg/m2), is a rough guide commonly used to classify these conditions. For adults, the World Health Organization defines overweight when BMI is greater or equal to 25, and obesity for a BMI higher than or equal to 30, with three levels of obesity (World Health Organization, 2016).
In 2016, more than 1.9 billion adults aged 18 years and older were overweight worldwide, and among these adults, over 650 million adults were obese, which supposes 39% of adults worldwide to be overweight and about 13% of the world’s adult population was obese (World Health Organization, 2016). In the same year, in Mexico, there was a combined prevalence of overweight and obesity of 71.2%, with higher prevalence among women. Obesity affected 38.6% of women and 27.7% of men, while overweight affected 37.0% of women and 41.7% of men (Morales et al., 2016).
The main cause of obesity and overweight is an energy imbalance between calories consumed and calories expended, as result to the increased intake of energy-dense foods that are rich in fat, and a decrease in physical inactivity due to the increasingly sedentary habits. The most common health consequences of overweight and obesity are cardiovascular diseases, diabetes, musculoskeletal disorders and increased risk of cancer, all correlated with the increase of BMI. In addition, childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood, with other specific consequences like hypertension, insulin resistance, and physiological effects (World Health Organization, 2016).
The mechanisms of metabolic alterations that explain pathophysiology of obesity are chronic imbalance of energy, high increase of adipocytes, local hypoxia, adipocyte apoptosis, increase of reactive oxygen species, oxidative stress of the endoplasmic reticulum and mitochondrial dysfunction and induction of pro-inflammatory responses (Vega-Robledo & Rico-Rosillo, 2019).
The immune cells embedded in the adipose tissue also suffer alterations. Macrophages increase as response of the pro-inflammatory and pro-oxidative environment, which recruits more of these cells. Some monocytes are attracted to the tissue, so local macrophages proliferate, with the
phenotype M1 (the classical or pro-inflammatory, instead of the M2, which is present in non-obese conditions and has anti-inflammatory function), thus secreting IL-1, IL-6 and TNF (Travers et al., 2015). Also, obesity increases the arrival of T cytotoxic and T helpers, with secretion of IFN-g, IL- 2 and TNF-a, which stimulates the production of innate lymphocytes type 2 and T regulators (Lynch et al., 2015). Finally, the accumulation of fatty acids in adipocytes originates cell damage, activating pro-inflammatory cascades. (Hotamisligil, 2017).
1.5.2 Consequences of maternal obesity
Obesity has the potential to affect future generations starting on pregnancy and during lactation, when metabolic changes related to maternal obesity can alter the constituents of milk. The chronic inflammation of obesity may promote the over-response of placenta to inflammation, leading to the accumulation of macrophages on it, as well as the secretion of pro-inflammatory molecules.
Furthermore, adipose tissue is responsible for the production of adiponectin and leptin, which are hormones related to hunger regulation, both related to the development of obesity and metabolic syndrome, but also affect immune cells: adiponectin acts as an anti-inflammatory agent, while leptin promotes inflammation. Also, phagocytosis and release of reactive oxygen species are reduced in obese mothers’ milk (Morais et al., 2019)
Obesity alters more than 29 immunological factors in milk. Among these changes are the C- reactive protein (CRP), leptin, IL-6, insulin, TNF-a, ghrelin, adiponectin, and obestatin, which have influence over genetic, metabolic and epigenetic processes. The increase of leptin and adiponectin, as well as insulin-like growth factor 1 (IGF-1) and ghrelin on milk from obese mothers, promote the development of overweight on their offspring. However, other immunological factors are not reported yet, such as IL-8 and IL-1B. Neutrophils and stem cells increase and decrease, respectively. In addition, the phagocytes on milk (neutrophils-macrophages) comprise 46.6% (±
19.35) on underweight women, 60.24% (± 6.93) on normal weight-women and 55.55% (± 16.16) on obese ones. However, little or none is reported about other cellular subpopulations, such as lymphocytes (Erliana & Fly, 2019).
1.6 Thesis outline 1.6.1 Justification
Worldwide in 2016, 40% of women were overweight (body mass index >25), and 15% suffered from obesity (body mass index >30) (World Health Organization, 2016). In Mexico, more than 70% of women suffer from overweight or obesity (Morales et al., 2016). Even though immune profiles are well studied in healthy people, there is scarce information about the impact of these conditions over immune profiles.
Breast milk is a continuous source of nutrients and immune factors (bacteria, leukocytes and immunomodulatory molecules) for the baby, from birth until the end of lactation. Most of these human cells and molecules have come from bloodstream. Leukocyte total values and relative frequencies on human milk vary along with the lactation (Trend et al, 2015), but also during infective processes, where these profiles can vary increase in different grades (Hassiotou et al., 2013). However, most of the analyses on leukocyte populations in human milk only included mothers with normal BMI. Erliana and Fly (2019) showed that all studies about milk composition from obese women have been done by separate, focusing on a specific factor at a time for the immunological factors, and not included many subpopulations of leukocytes.
Current available information does not give insights in the specific changes on relative frequency of leukocytes in colostrum that contrast with peripheral blood from the same mothers. This is important to know better if leukocyte migration to milk is a regulated process or not.
Considering all this, it emerges the necessity to study if there are changes in leukocyte proportions between colostrum and peripheral blood, especially in overweight and obesity, where the information on the topic is scarce. The present research focuses on investigate the abundance of leukocyte subpopulations in peripheral blood of lean and obese mothers, and compare these frequencies with their counterpart in colostrum, but also compare against the other study group.
1.6.2 Hypothesis
Frequencies of leukocyte subpopulations differ between peripheral blood and breast milk; and obesity promotes an alteration in the proportions of leukocytes in mother’s peripheral blood that is also present in breast milk.
1.6.3 General objective
To determine changes in the proportions and characteristics of leukocyte subpopulations between peripheral blood and colostrum that can be altered by obesity.
1.6.4 Specific objectives
1. To characterize and immune cell populations present in peripheral blood and colostrum from mothers with healthy weight and obese mothers, using flow cytometry.
2. Compare the relative frequencies obtained of leukocytes in peripheral blood and colostrum, and determine if they are different between samples and study groups.
3. Compare the parameters of relative size, complexity and expression of cell markers between samples and groups to determine patterns of change.
2. Materials and methods
2.1 Brief introduction to flow cytometry
Flow cytometry is a multiparametric technique that lets the immunophenotypification of cellular suspensions. It is multiparametric because it lets the simultaneous measurement of several parameters. The immunophenotypification refers to the use of antibodies against specific antigens that will be the target of identification and characterization. Flow cytometry is based on a system of lasers, mirrors and filters that will detect signals from the antibodies. The antibodies used in flow cytometry are often conjugated with fluorochromes, molecules that absorb and emit light in specific wavelengths. These wavelengths are filtered and detected as the corresponding signal of the antibody that the fluorochrome is conjugated to. Besides multiparametric feature, flow cytometry has the advantage of working with cellular suspensions, which lets the analysis of hundreds or even millions of cells in a single sample. This way, rare populations can be detected without major inconvenient, and more reliable results are achieved by analyzing more cells than with similar methods (Givan, 2001).
2.2 Antibody titration
Antibody titration is part of the optimization process of flow cytometry panels. It consists in finding the volume of antibody needed to get the best signal. To do so, the cells are processed the same they would be processed in a real experiment. Different volumes of antibody were tested, from manufacturer’s volume (5 µL), to one-half (2.5 µL) and one-quarter (1.25 µL) for one million cells.
Antibodies were tested on peripheral blood mononuclear cells (PBMCs) and granulocytes. The
cells were obtained from volunteer donors’ peripheral blood using Ficoll paque®. The ratio was respected, so less than 5 µLof antibody were used in some cases.
From every stained sample as well as for the unstained control, the median fluorescence intensity (MFI) is obtained using FlowJo®. Then, the Separation Index (SI) is calculated using the following formula:
= −
(84 − )/0.995
Finally, we made a graph with the obtained SI values for every dilution, with antibody volume on the X-axis, and SI on the Y-axis. The best dilution is such with the highest separation index.
2.3 Study groups
Samples were obtained from Hospital Regional de Alta Especialidad Materno Infantil, located in Guadalupe, Nuevo Leon. Table 1 summarizes the main characteristics that define each group. The general inclusion criteria are:
Women between 18 and 34 years old.
Independently of the delivery mode: vaginal or C-section
Birth at term (>37 weeks of gestation)
Samples taken within 2 days of birth
Table 1. Main characteristics of the study groups included on this project.
Group BMI (kg/m2) Conditions
Lean 18.5-24.9 Healthy (lean), as control
Obese >30 Obese, without type 2 diabetes or
hypertension
2.4 Sampling, storage, and transport
Samples were collected at the Hospital Regional de Alta Especialidad Materno Infantil in Guadalupe, N.L. Mexico. All samples were obtained following signed informed consent, between 6 and 10 am. All processes were reviewed and approved by the Ethics Committee (Protocol:
CarMicrobiolHum2018. Caracterización de la microbiota en la leche humana temprana.
Monterrey, NL, México, Versión 1.14, Enmienda 2,del 8/jul/2020).
Peripheral blood: a 3-to-4-mL sample was taken from a donor, according to the study groups above by dr. Eduardo González-Dávila and dr. Jorge Moreno through peripheral venipuncture using a hypodermic needle (22G) in a purple-cap K2EDTA Vacutainer® tube, and put immediately on ice.
Breast milk: a 1-to-3-mL sample was taken from a donor, assisted by Dr. Eduardo González- Dávila, dr. Jorge Moreno, and personal from the Department of Breastfeeding. After handwashing, the donor bent to the front, then made a round massage to the breast to facilitate milk ejection.
The thumb was put above the nipple and the index below forming a C shape, and then by making a soft pressure on the nipple, milk was extracted directly on a 15 mL polypropylene tube (Falcon®).
The samples were immediately put on ice until their collection by laboratory personal.
All available data from samples were recorded, and include birth date, delivery mode, newborn’s gender, milk volume, respective study group, and any remarkable visual feature (color, viscosity).
All samples were kept on ice until their processing.
2.5 Leukocyte enrichment from colostrum
Between 700 µLand 1000 µL of colostrum were used for the cell enrichment. The samples were transferred into a 1.7 mL microcentrifuge polypropylene tube. The remaining milk was labeled and frozen at -20°C. Samples were centrifuged at 400 rcf for 15 minutes at 4°C. The supernatant (fat layer and skim milk) was transferred to another 1.7 mL polypropylene tube, and stored at - 20°C for cytokines analysis. The cell pellet was washed twice at the same previous conditions by re-suspending in 1000 µL of PBS (Gibco® catalog 2053135) supplemented with 2% fetal bovine serum (FBS). After the last wash, the pellet was re-suspended in 1000 µL of PBS + 2% FBS, and 10 µL were used for counting using 0.4% trypan blue solution staining with a hemocytometer.
2.6 Colostrum-enriched cells staining
Samples were kept on ice until staining with antibodies for flow cytometry. Between 0.2 and 1x106 cells were used for staining, and the same number of cells were kept as unstained control. In those samples with less than 106 cells, the available number of cells were stained with the equivalent volumes of antibodies for 1x106 cells, according the following steps:
The cells were transferred into a 1.7 mL microcentrifuge polypropylene tube and centrifuged at 400 rcf for 5 minutes at 4°C. The supernatant was discarded, and the pellet was resuspended in the antibody Master Mix, prepared with 1x PBS + 2% FBS + antibodies, and then incubated for 30 minutes on ice, covered from direct light. The Master Mix was prepared with the titrated
volumes of the following antibodies: mouse anti-human CD2-APC (BD® cat. 560642), mouse anti-human CD16-APC-H7 (BD® cat. 560195), mouse anti-human CD19-V450 (BD® cat.
560353), mouse anti-human CD36-PE (BD® cat. 555455), mouse anti-human CD45-V500 (BD®
cat. 560777), and rat anti-human CD294-Alexa Fluor 647 (BD® cat. 558042). The final volume for staining 106 cells was 100 µL using PBS + 2% FBS.
900 µL of PBS + 2% FBS were added after the incubation and the samples were centrifuged as before. The supernatant was discarded, and the pellet resuspended in 90µLof PBS + 2% FBS.
10 µL of propidium iodide (BD® catalog 556463, at 50ug/mL) per 106 cells (according to manufacturer’s protocol) were added 10 minutes before analyzing with the flow cytometer.
Samples were kept on ice until their analysis. 200 µL of PBS + 2% FBS were added immediately before running on flow cytometer.
2.7 Peripheral blood staining
Peripheral blood was directly processed using 50 µL aliquots. The aliquots were transferred into 1.7 µL polypropylene microcentrifuge tubes, and the antibodies (same as described above) were directly added to the aliquots and gently mixed. Incubation was performed for 30 minutes on ice, covered from direct light. Then, both stained and unstained samples were lysed using 450 µL of 1x ammonium chlorine lysis buffer, incubating at room temperature, covered from direct light.
After 10 minutes of incubation, 1 mL of PBS + 2% FBS was added and samples were centrifuged at 400 rcf for 10 minutes at 4°C. Pellets were resuspended in 95 µL PBS + 2% FBS + 5 µL of propidium iodide solution. Samples were put on ice for 10 minutes, and 200 µL of PBS + 2% FBS were added before analyzing on flow cytometer.
For propidium iodide compensation controls, 50 µL of peripheral blood were put 5 minutes at 60°C, then 50 µL of fresh peripheral blood were added, and lysis was performed using 900 µL of 1x ammonium chlorine lysis buffer and processed as described above. After 10 minutes of incubation, 500 µL of PBS + 2% FBS were added and samples were centrifuged at 400 rcf for 10 minutes at 4°C. Pellets was resuspended in 95 µL PBS + 2% FBS + 5 µL of propidium iodide solution. 200 µL of PBS + 2% FBS were added immediately before reading on flow cytometer. The remaining whole peripheral blood of every sample (between 1 and 3.5 mL) was centrifuged at 1000 rcf for 10 minutes at room temperature. The serum was divided in 200 µL aliquots, storing between 400 µL and 1 mL from every sample at -20°C.
2.8 Sample acquisition
Samples were analyzed using a BD® FACSCelesta flow cytometer with BD® FACSDiva 8.0 software. Compensation controls were prepared with Compensation Beads (BD® Anti-mouse Ig,K Neg Control compensation catalog 552843, lot 9233251), according to manufacturer’s instructions. Briefly, single-stained controls are prepared with one drop of positive beads and one drop of negative beads, resuspended in 200 µL of 1x PBS + 2% FBS, and the titrated volume of antibody for 106 cells. After 30 minutes of incubation, 1 mL of 1x PBS + 2% FBS is added to each stained tube. The tubes are centrifuged at 200 rcf for 10 minutes at room temperature, and the pellet is resuspended in 200 µL of 1x PBS + 2% FBS. For negative control, one drop of negative beads is resuspended in 200 µL of 1x PBS + 2% FBS.
30000 single events from peripheral blood and 50000 events from colostrum-enriched cells were recorded from every sample, with event threshold adjusted on 35000 for peripheral blood and 28000 for colostrum. Further analysis of the resulting files was done using FlowJo® version 10.
2.9 Gating strategy
Before analyzing the samples, they were compensated on FlowJo®. The analysis was based on Faucher et al. (2013) and Trend et al. (2015) gating strategy. The gates are done in the following order:
1. Total leukocyte gating. Initial visualization of the samples in a FSC vs. SSC gate.
a. Peripheral blood. With proper lysis, the three leukocyte subpopulations are distinguishable. Here, debris is easily excluded by selecting all except the dense population with low FSC and SSC. This gate must contain the three leukocyte subpopulations and go up to the limits of the window.
b. Colostrum. The gate must exclude the debris area on the left down corner, so it includes everything else.
2. Singlets gating. The leukocyte singlets are gated on an FSC-A vs. FSC-H plot, selecting the diagonal where both parameters are almost equal, this is, where the area (A): height (H) ratio is almost 1:1.
3. Gating of live cells. Live cells are gated on an PI vs SSC-A plot. This is done on the unstained sample’s singlets population. The positive, which corresponds to dead cells, must include up
to 2% of positive events on the unstained sample. Then, this gate is applied to the stained sample by dragging on it.
4. Live leukocytes gating. On the unstained live cells plot in CD45 vs. SSC-A, a gate is done to the right (where the positive population for CD45 would appear) that contains up to 2% of
“positive” events. This gate must go from the upper limit to the bottom, and to the right edge.
It will be applied to the stained sample.
5. Visualizing the live leukocytes plot in SSC-A vs. CD16, four gates are done:
a. Neutrophils. The population with high SSC-A and positive for CD16 is gated. Its limits are the right edge, the bottom is where the positive populations for CD16 begin, the left and top limits are where this population ends.
b. Gate B. Low SSC-A population positive for CD16. Its right limit is the end of the Neutrophils’ gate, the bottom is where the CD16 starts, the left limit is where the populations on gate C and it begin, and the top is equal to the Neutrophils’ gate.
c. Gate C. Low and medium SSC-A and negative for CD16. The right limit is where the population on this gate ends, its bottom is on the lowest limit of the CD16 axis, its left limit is where the populations on gate B and it begin, and its top is the bottom of gate B, or where the positive for CD16 starts.
d. Gate D. Medium to high SSC-A and negative for CD16. The right limit is on the right edge, its bottom is the lowest value of the CD16 axis, its left limit is the right population with medium SSC-A (it overlaps with gate C), and its top is the bottom of gate B and neutrophil’s gate, or where the positive for CD16 starts.
The top of gates C and D, or the bottom of gate B and Neutrophils’ gate, is done on the unstained sample. The other limits of the gates C and D also can be done on the unstained sample, but they need to be adjusted on the stained sample. The Neutrophils’ and B gates can only be done on the stained sample.
6. Inside gate B: CD16 positive monocytes, and cytotoxic T cells and natural killers. Visualizing the plot on CD36 vs. CD2 and CD294, two gates are done:
a. CD16 positive monocytes are gated on the disperse population positive for CD36 but negative for CD2 and CD294.
b. Cytotoxic T cells and natural killers are gated on the population positive for CD2 and CD294 but negative to CD36.
7. Inside gate C, two new gates are done:
a. Gate E. Two populations are gated inside E on CD45 VS. CD2 and 294. The upper population positive for CD45 and CD2 and CD294, and the population with low expression of CD45 and medium expression of CD2 and CD294.
b. Gate F. The population high positive for CD45 and negative for CD2 and 294. It is below the big population of gate E.
8. Inside gate E, visualizing the plot on CD45 vs. SSC-A two populations are gated:
a. Basophils. The population to the left, with medium-to-low SSC-A and low expression of CD45.
b. Non cytotoxic T cells. The population to the right, with low SSC-A and positive for CD45.
9. Inside gate F, visualizing the plot on CD19 vs. CD36, two populations are gated:
a. Gate G. The population to the left, negative to CD19 and with all the range for CD36.
b. Gate H. The population to the right, positive to CD19 but negative to CD36.
10. Inside gate G, two populations are gated:
a. CD16 negative monocytes are gated visualizing the plot on CD45 vs. CD36, selecting all the cells positive to CD36.
b. Myeloid precursors
11. Inside gate H, B cells are gated visualizing the plot on CD45 vs. SSC-A, selecting all the population positive to CD45 with low SSC-A.
12. Inside gate D. Visualizing the plot on CD45 vs. SSC-A, two populations are gated:
a. Immature granulocytes are gated as the population with negative or low expression of CD45 and high SSC-A.
b. Gate I is done by gating the population positive to CD45 with high SSC-A, avoiding a big population with low SSC-A.
13. Inside gate I, visualizing the plot on CD2 and CD294 vs. SSC-A, the eosinophils are gated selecting the population with high SSC-A and low or medium expression of CD2 and CD294.
2.10 Data analysis and statistics
The absolute number of leukocyte subpopulations per mL were calculated using the obtained relative frequencies of leukocytes and leukocyte subpopulations, and the obtained total colostrum-enriched cells counted with the hemocytometes. The following formulas were used:
= ( ℎ ) ∗ (% 45 )
= ( ) ∗ (% 45 )
All statistical analyses were done using Minitab 19. For all of them, α was considered on 0.05.
First, a normality test (Ryan-Joiner) was performed to know the data distribution. From those parameters with normal distribution (P value > 0.05), two-samples t tests were performed. From those parameters without normal distribution (P value ≤ 0.05), Mann-Whitney tests were performed.
Four types of comparisons were done: comparison of peripheral blood against colostrum from lean mothers, comparison of peripheral blood against colostrum from obese mothers, peripheral blood from lean against peripheral blood from obese mothers, and colostrum from lean mothers against colostrum from obese mothers.
Finally, all graphs from every parameter were done using Prism – Graphpad. Two types of graphs were done for relative frequencies of leukocytes in peripheral blood and colostrum from both groups: a dot plot showing mean and SD, and a pairwise comparison linking peripheral blood and colostrum from every donor mother. For cell parameters and fluorochromes, bar graphs were done using the mean for MFI, including SD. These bar graphs were done combining data from both groups as no major differences were found between groups.
3. Results
3.1 Development of protocols 3.1.1 Antibody titration
In this section, we applied a dilution of the concentration of each antibody in the panel in order to obtain the largest possible separation between antibody-stained and unstained populations. This is evaluated as a ratio of the Median Fluorescence Intensity (MFI) of the stained population for a specific antibody dilution, over the MFI of the unstained population, and is presented as a separation index (SI). The objective was to evidence the highest SI for each antibody titrated, which corresponds to the largest separation between stained MFI and background, and the antibody dilution to use in the panel. Titrating antibodies forms part of optimizing a flow cytometry experiment in order to improve population identification and eventually save reagents and money while performing a large output experiment.
Figure 1 shows the various SI according to the tested volumes of antibodies for each antibody tested. These volumes correspond to the manufacturer’s recommended volume, one-half, and one quarter of manufacturer’s recommendations for each antibody, and the staining was performed on relevant cell samples in each case, such as granulocytes (orange line) and PBMCs (blue line). Panels a) and d) show CD2-APC and CD36-PE, respectively. The titration of these antibodies show the same SI trends with the highest SI on the second dilution (half of the manufacturer’s recommendation). Panel c) shows CD19-V450 graph, where the highest SI is the manufacturer’s volume. The opposite happens with CD294-Alexa Fluor 647, where the highest SI is with the one-quarter dilution. Panel b) shows CD16-APC-H7, the highest SI was one-half of manufacturer’s dilution, while for PBMCs it was the lowest dilution. CD45-V500 was also tested with granulocytes and PBMCs. The SI of PBMCs was on the manufacturer’s volume, while for granulocytes it was the lowest volume. For these two last antibodies, the one-quarter dilution was chosen; in the case of CD16, because it was the best dilution for PBMCs, which are not so abundant, and a higher amount of antibody would decrease their brightness. And for CD45, to avoid reducing the signal on granulocytes.
Figure 1. Separation index (SI) graphs for each antibody-fluorochrome. Blue lines correspond to PBMCs and red lines correspond to granulocytes. a) CD2 - APC; b) CD16 - APC-H7; c) CD19 -
V450; d) CD36 - PE; e) CD45 - V500; f) CD294 - Alexa Fluor 647.
From the obtained graphs on figure 1, we selected those volumes with the highest SI for further experiments. All the information of the antibodies and dilution is on table 2.
0.0 0.5 1.0 1.5 2.0 2.5 3.0 0
5 10 15 20
Antibody (uL)
0.0 0.5 1.0 1.5 2.0 2.5 3.0 260
280 300 320 340 360
Antibody (uL)
Separation Index
a) b)
c) d)
e) f)
CD2-APC CD16-APC H7
CD19-V450 CD36-PE
CD45-V500 CD294-AF 647
Table 2. Antibody summary and best volume for staining one million cells.
Antibody Isotype Brand Catalog Lot Type of cells titrated
Best
volume per 106 cells Mouse anti-human
CD2 APC
conjugated
IgG1, K BD 560642 8043611 PBMCs 2.5 µL
Mouse anti-human CD16 APC-H7 conjugated
IgG1, K BD 560195 8190957 PBMCs and Granulocytes
1.25 µL
Mouse anti-human
CD19 V450
conjugated
IgG1, K BD 560353 8099649 PBMCs 5 µL
Mouse anti-human
CD36 PE
conjugated
IgM, K BD 555455 8201569 PBMCs 2.5 µL
Mouse anti-human
CD45 V500
conjugated
IgG1, K BD 560777 8208783 PBMCs and Granulocytes
1.25 µL
Rat anti-human CD294 Alexa Fluor 647 conjugated
IgG2a, K BD 558042 8179962 Granulocytes 1.25 µL
3.1.2 Test of complete flow cytometry panel
In this section, the optimized panel of antibodies was tested with two different types of samples.
We directly stained peripheral blood without previous processing of the blood, and stained enriched cells from colostrum (using the optimized protocol presented in Appendix C). Then, we overlapped the obtained evidenced leukocyte populations from each sample type to identify possibly different patterns and trends. The detailed gating strategy is on Materials and Methods.
Here are the summarized parameters considered for each leukocyte subpopulation.
- Non-classical monocytes. CD16+, CD36+, CD2/294- - Cytotoxic T cells and NK. CD16+, CD36-, CD2/294+
- Basophils. Intermediate (up to 200,000) SSC-A, CD2/294+, CD45+ - Non-cytotoxic T cells. CD16-, CD2/294+, CD45+
- B cells. CD16-, CD2/294-, CD36-, CD19+, CD45+
- Classical monocytes. CD16-, CD2/294-, CD19-, CD36+, CD45+ - Eosinophils. High SSC-A, CD16-, CD45+, CD2/294+
- Immature granulocytes. High SSC-A, CD16-, CD45low - Neutrophils. High SSC-A, CD16+
Once we identified the optimal volumes of antibody to use, we tested the full panel on peripheral blood. Figure 2 shows the gating strategy on a representative peripheral blood sample. Panel 1 shows the selection of leukocytes based on their FSC-A vs. SSC-A pattern, and discard of debris (low FSC-A and low SSC-A). Panel 2 shows the selection of single cells for their pattern of FSC- A vs. FSC-H. Panel 3 shows the selection of live cells (PI-). Panel 4 shows the gating of leukocytes (CD45+ cells). Here, the population did not show a high shift on CD45, so the gate was set to only excluding the truly negative events for CD45.
This population was put on SSC-A vs. CD16 (Panel 5). From this panel, neutrophils and gates B (panel 6), C (panel 7) and D (panel 8) are gated. Panel 6 shows the gating of non-classical monocytes (CD36+) and cytotoxic T cells and NK (CD2/294+). Panel 8 shows the gating of immature granulocytes and gate I (panel 9). Panel 9 shows the gating of eosinophils. Panel 7 shows the gating of gates E (CD2/294+, including the population with low expression of both CD45 and CD294) and gate F (CD2/294-, CD45+). Panel 10 shows the gating of basophils and non- cytotoxic T cells inside gate E. Panel 11 shows the gating of gates G (CD36+, CD19-) and gate H (CD19+, CD36-). Panel 12 shows the gating of classical monocytes, and panel 13 shows the gating of B cells.
The original panel only distinguished between basophils and eosinophils for their SSC-A value as
“intermediate” and “high”, respectively (Faucher et al., 2007). To solve this subjectivity, on panel 5, we set the limit of gate C on 200,000 for SSC-A. This value correspond to the median of neutrophils, which comprise the most abundant granulocyte subpopulation. Inside gate C (on panel 7), it was necessary to include the population with low expression of CD45 with low SSC-A as part of the gate. Otherwise, no high-SSC-A events (identified as basophils) are present inside the gate E (panel 10).
Figure 2. Ancestry-type analysis of the full antibody panel in peripheral blood. Panels 1 to 13 were successively analyzed. Panel 1: cells gating and exclusion of debris. Panel 2: single cells gating.
Panel 3: live cells gating. Panel 4: gating of leukocytes (CD45+ cells). Panel 5: gating of neutrophils and gates B, C and D. Panel 6: gating of non-classical (CD16+) monocytes, and
cytotoxic T cells and NK. Panel 7: creation of gates E and F. Panel 8: gating of immature granulocytes and creation of gate I. Panel 9: gating of eosinophils. Panel 10: gating of basophils
and non-cytotoxic T cells. Panel 11: creation of gates G (CD36+/CD19-) and H (CD19+/CD36-). Panel 12: gating of classical (CD16-) monocytes. Panel 13: gating of B cells
The same gating strategy was followed for analyzing populations identified through the full antibody panel applied to colostrum-enriched cells (figure 3). In samples of colostrum-enriched cells, a population of low SSC-A and wide range of FSC-A was present in all samples (panel 1), which is not present in peripheral blood. This population remained in the sample despite the enrichment. In some samples, this population was so big that it overlapped the leukocyte subpopulations, but it does not affect further characterization of subpopulations, it is eliminated in panel 4, where CD45- events are excluded. We applied the same gating strategy to the one applied to peripheral blood, including setting the leukocytes gate on “live cells”, and SSC-limits to identify basophils and eosinophils. One difference with peripheral blood samples is on panel 7, which corresponds to gate C. In colostrum-enriched cells, it is not necessary to include the population with CD45low expression on gate E in order to visualize presumptive basophils there (panel 10).
In colostrum-enriched cells, both subsets of monocytes were hard to find. Non-classical (CD16+) were identified because gate B (panel 6) includes two specific antibodies, CD36 in X axis and CD2/294 in Y axis. Here, it was clear that those events positive for CD36 but negative for CD2/294 are the non-classical (CD16+) monocytes. However, gate G (panel 12) includes CD36 but also CD45. In colostrum, three different populations appear on that gate, with different patterns of expression for CD45, so it was unclear which one of these three populations contained the classical (CD16-) monocytes. As they were only found in peripheral blood, no statistics was performed for this population.
Figure 3. Ancestry-type analysis of the full antibody panel in colostrum-enriched cells. Panels 1 to 13 were successively analyzed. Panel 1: cells gating and exclusion of debris. Panel 2: single cells
gating. Panel 3: live cells gating. Panel 4: gating of leukocytes (CD45+ cells). Panel 5: gating of neutrophils and gates B, C and D. Panel 6: gating of non-classical (CD16+) monocytes, and
cytotoxic T cells and NK. Panel 7: creation of gates E and F. Panel 8: gating of immature
granulocytes and creation of gate I. Panel 9: gating of eosinophils. Panel 10: gating of basophils and non-cytotoxic T cells. Panel 11: creation of gates G (CD36+/CD19-) and H (CD19+/CD36-). Panel
12: gating of classical (CD16-) monocytes. Panel 13: gating of B cells
We made an overlap of the same gates using peripheral blood and colostrum-enriched cells from a random lean mother (figure 4). In general, peripheral blood populations are more defined (show less variation, and appear more defined) than those in colostrum are. Panels 1 and 2 are very alike. Panel 3 shows events that are more positive for PI staining in colostrum-enriched cells than in peripheral blood. Panel 4 shows the higher expression of CD45-V500 in granulocytes from colostrum-enriched cells than their counterpart in peripheral blood, whereas lymphocytes expression of CD45 are similar between both sample types. In panel 5, the population of neutrophils from peripheral blood are more defined than those from colostrum. In addition, they seem to express higher levels of CD16 in blood than in colostrum. Panels 6 and 7 are similar between samples.
Panel 8 shows more events included inside gate I in colostrum than for peripheral blood, but these events were not CD2/294+ (eosinophils) as showed in panel 9. On panel 10 we can appreciate that basophils from colostrum are more positive for CD45 than those from peripheral blood, whereas non-cytotoxic T cells are equal in both samples. Panel 11 shows different patterns for CD19 and CD36 expression in both samples. While in peripheral blood the populations are long and defined, those from milk are more diffuse. There is also a population intermediate in colostrum-enriched cells that goes diagonally on the panel. Finally, panel 13 shows that B cells from colostrum and peripheral blood are very alike in the expression of CD45.
Figure 4. Overlapping of peripheral blood (red) and colostrum (blue) successive panels used on gating strategy.
3.2 Analysis of clinical samples
Once the processing and analysis protocols were established, we set-up to investigate leucocyte populations in lean and obese young mothers’ peripheral blood and colostrum-enriched cells. In total, we received samples from 22 donor mothers (peripheral blood and colostrum) from each study group. Table 3 shows the summary of all clinical data obtained from donors, and table 5 shows the descriptive statistics of received samples per group. Despite analyzing all samples using the exact same protocol, quality of data forced us to delete various samples from the final analysis. In the end, 19 lean mothers and 15 obese mothers were included in statistical analysis, as some colostrum-enriched cell samples evidenced low quality hampering further analysis. We considered as a colostrum sample as low quality when less than 10000 CD45+ events could be obtained.
Table 3. Clinical characteristics of donor mothers.
Clinical characteristics Value Lean Obese P value Body Mass Index
(mean ± SD [range]) Kg/m2
23.7±1.49 [20-25]
33.5±3.67 [30-42]
<0.0001 Gestational age
(mean ± SD [range]) Weeks
39.2±0.98 [37-41]
38.6±1.08 [37-41]
0.204
Delivery mode
Vaginal 15 15 n/a
C-section 7 7 n/a
Baby gender
Male 12 14 n/a
Female 10 8 n/a
Table 4 shows the mean obtained volumes with SD of peripheral blood and colostrum from lean and obese mothers. It also contains the mean and SD of enriched cells from colostrum, expressed in cells per mL, and the percentages with SD of live cells and CD45+ events recorded on the flow cytometer. On average, colostrum from lean mothers had more total cells than obese ones, with means of 5.9 and 4.0 million cells per mL of colostrum, respectively. SD in enriched cells from colostrum increased due to a sample with very high concentration of cells (39.8 million cells per mL of colostrum). Both groups have similar mean values of live cells (91 in lean and 90.2 in obese mothers, respectively). Colostrum from lean mothers contained on average a 20% more leukocytes (CD45+ cells) than colostrum from obese mothers.
Table 4. Mean with SD for received volumes of samples, obtained millions of cells per milliliter of colostrum processed, percentage of live cells and percentage of CD45+ events in colostrum.
Lean Obese P value
Peripheral blood received, in mL (mean ± SD [range])
2.4±0.82 [1.5-4]
2.1±0.95 [0.5-4]
0.49
Colostrum received, in mL (mean ± SD [range])
2.2±0.96 [0.5-5]
1.4±0.73 [0.4-3]
0.02
Millions of enriched cells obtained per mL of colostrum (mean ± SD
[range])
5.9±90 [0.6-39.8]
4.0±5.50 [0.4-21.3]
0.72
Percentage of live cells in colostrum
(mean ± SD [range])
91.0±7.10 [75.3-98.9]
90.2±8.50 [68.6-97.8]
0.74
Percentage of CD45+ events in colostrum (mean ± SD [range])
37.6±27.40 [5.2-86.4]
30.7±23.60 [6.9-76.9]
0.49
The following sections show the analysis of the nine leukocyte subpopulations identified with this panel. From each leukocyte subpopulation, we generated two types of graphs from the relative frequency of the target leukocyte population to the total leukocytes in the sample. First, a dot plot shows the distribution of each individual samples, including the group mean value and standard distribution, then a second graph pairs peripheral blood and colostrum-enriched cells % from each sample to show trends in distribution between both sample types.
3.2.1 Immature granulocytes
In blood, immature granulocytes were present with an average proportion of 0.53% from total leukocytes in lean mothers, versus 0.63% in obese mothers. The SD increased in the group of obese mothers compared to the group of lean mothers (0.53 and 0.74, respectively). Of note, one lean mother had an exceptionally high percentage immature granulocyte (3.40%), which all other samples had percentage between lower than 1%, which may have skewed the mean. In colostrum, the mean frequency of immature granulocytes was of 0.99% for lean mothers and 0.52% for obese mothers. Despite observing a mean percentage of immature granulocytes in colostrum of
obese mothers compared to lean mothers, this difference was not statistically significant (P value
= 0.35).
When comparing groups, we observe that in lean mothers’ colostrum there is a lower mean of immature granulocytes compared to peripheral blood (0.52 and 0.63%, respectively) with no statistical difference. In obese mothers, the trend is the opposite, as colostrum has 0.99% of immature granulocytes whereas peripheral blood has 0.52%, with no statistical difference.
Since immature granulocytes in both samples showed non-normal distribution (P value for Ryan- Joyner test >0.1), the analyses were performed used non-parametrical test (Mann-Whitney), which uses medians. The medians of colostrum were 0.36 and 0.37 respectively for lean and obese groups; therefore, there is no statistically significant difference. Despite
In figure 5 panel a) we can see the distribution of individual samples for peripheral blood and colostrum, with mean and SD. In panel b) we see the trend between peripheral blood and colostrum on each group sample.
Figure 5. Relative frequency of immature granulocytes. Panel a) shows dot plots with mean and SD. Panel b) shows pairwise comparison with lines connecting each mothers' pair of samples.
Table 5 summarizes the information of immature granulocytes in each group and sample.
Table 5. Descriptive statistics for immature granulocytes.
Group and sample Mean SD Median Minimum Maximum
Lean, peripheral blood 0.52 0.74 0.37 0.01 3.40
Lean, colostrum 0.63 0.37 0.5 0.24 1.79
