Flagellar-dependent motility (Bray, 2001; Beneke et al., 2019) is key for transmission of unicellular Leishmania parasites, causative agents of the leishmaniases. These infections represent the world’s ninth largest infectious disease burden with an expanding territory endemic to 98 countries that threatens 350 million people globally (Alvar et al., 2012). Like many vector-borne parasites, life cycle stage-specific differentiation is observed, affecting both Leishmania body shape and flagellar morphology (Pulvertaft and Hoyle, 1960; Bates and Rogers, 2004; Ambit et al., 2011). Notably, flagellar length changes dramatically in Leishmania parasites relative to other flagellated microorganisms. Procyclic promastigotes’ (PCF) cell bodies are 10–12 μm long, with a flagellum of approximately the same length, while human-infective metacyclic promastigotes’ (META) cell bodies are 8–10 μm in length, with a flagellum 20 μm long (Sacks and Perkins, 1984; Video 1). We summarise the key stages in the Leishmania mexicana life cycle in Figure 1—figure supplement 1.

Video 1

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Studying the three-dimensional swimming patterns of motile flagellates gives insight into regulation of the flagellar beat and details of the cells’ navigation strategy. The physics of microorganism swimming and navigation has been considered extensively in the context of bacterial motility, where biased random walks are used to counter the randomisation of Brownian motion (Berg and Brown, 1972; Taktikos et al., 2013; Son et al., 2016). Leishmania cells are an order of magnitude larger in each dimension than typical model bacteria, and rotational diffusivity scales inversely with the cell’s volume (Berg, 1993). In a medium with viscosity close to that of water, it takes a few seconds to randomise the orientation of an Escherichia coli cell, but over 200 s to randomise the orientation of Leishmania. The physical constraints that shape the response of these two micoorganisms are therefore fundamentally different. From a fluid dynamics perspective, both operate at low Reynolds number, but Brownian motion dominates life for the bacterial system, while it is negligible for Leishmania. Nevertheless, there are intriguing signs that the run-tumble locomotion characteristic of bacteria like E. coli has analogues in motile single-cell eukaryotes: Chlamydomonas reinhardtii algae exhibit run and tumble behaviour (Polin et al., 2009), and sperm from the sea urchin Arbacia punctulata exhibit sharp reorientation events amidst periods of helical swimming about a straight ‘run’ axis (Jikeli et al., 2015).

Chemotaxis has been observed in trypanosomatids, although the results have often been somewhat nuanced. In an early study of chemotaxis in L. mexicana, Leishmania major, and Leishmania donovani (Bray, 1983), it was found that promastigote cells move up gradients of sugars, serum, and some serum components. This work also found some evidence that peritoneal macrophages from mice were attracted by serum that had been ‘activated’ by exposure to L. mexicana. The hypothesis that the host and motile parasites interact by diffusing messengers is supported by a study that shows that Leishmania promastigotes are repelled by ATP, possibly allowing them to evade neutrophils and adapt for phagocytosis (Detke and Elsabrouty, 2008). Other authors have found evidence that promastigotes of Leishmania spp. chemotax towards sugars (Oliveira et al., 2000), though another contemporary study suggested that the directional behaviour is more appositely ascribed to osmotaxis, and is therefore chemically non-specific (Leslie et al., 2002). Intriguingly, the latter study speculates that chemotactic or osmotactic stimulus could play an important role in providing an orientational stimulus for the parasites that prevents them being excreted and allows them to navigate within the sandfly host. Barros et al., 2006 performed experiments in which they timed the duration between reorientation events in swimming cells, showing that Leishmania amazonensis promastigote cells respond to both osmo- and chemotaxis, and find that chemotaxis dominates the response for all but the highest stimulant concentrations. More recently, it was shown that Leishmania braziliensis and L. amazonensis show chemotactic responses towards stimulants including a repurposed cancer drug (methotrexate) (Díaz et al., 2013) and amino acids (Diaz et al., 2015). Optical tweezers studies on L. amazonensis (de Thomaz et al., 2011; Pozzo et al., 2009) showed that individual cells are attracted to higher glucose concentrations, and in the related trypanosomatids Trypanosoma cruzi and Trypanosoma rangeli, cells orient themselves towards tissue extracted from tsetse flies (their insect host) . Most recently, there have been exciting developments in the area of ‘social motility’ (collective motion) in Trypanosomes (Oberholzer et al., 2010; Schuster et al., 2017; Shaw et al., 2019), where it was shown that chemotactic behaviour is not just a single-cell phenomenon, but one that is exhibited in collective cell behaviour as well (DeMarco et al., 2020). Millimetre-scale groups of Trypanosoma brucei cells displaying social motility can change their swimming direction to avoid other colonies of their own species, but are attracted by chemicals diffusing from neighbouring colonies of E. coli.

Holographic microscopy has proven to be a versatile and powerful tool for investigating the dynamics of swimming microorganisms at high speed and diffraction-limited resolution (Katz and Sheng, 2010; Romero et al., 2012; Weiße et al., 2020; Heddergott et al., 2012; Wilson et al., 2013; Molaei et al., 2014; Jikeli et al., 2015; Zhang et al., 2018; Thornton et al., 2020). Three-dimensional tracking, using holographic microscopy or other techniques, unambiguously resolves chirality in the shape of objects and in the geometry of swimming paths. A long-standing question in the biological physics of the organelle at the heart of the flagellum focuses on the breaking of symmetry. The axoneme has a chiral structure in which dynein molecules on each doublet can attach to their clockwise neighbour, as viewed from the basal end of the axoneme (Afzelius et al., 1995). Several groups have examined whether this structural chirality of the axoneme results in chiral flagellar waveforms. Rodríguez et al., 2009 showed that the flagellum of T. brucei could produce waves of either left- or right-handed chirality, in both procyclic-form and bloodstream-form cells. Bargul et al., 2016 examined several species and strains of Trypanosoma and demonstrated that the chirality of the flagellar attachment can vary, even between strains of the same species, but the direction of the flagellum’s rotation is independent of these morphological features. This has also been seen in the absence of an accompanying cell body attached to a flagellum; the male microgamete of Plasmodium shows both right- and left-handed chiralities (Wilson et al., 2013).

Here, we compare and quantitatively define the swimming dynamics and parameters of two key L. mexicana life cycle stages and find evidence of unbiased chirality in swimming behaviour. We further demonstrate a macrophage-driven chemotactic strategy involving novel run and tumble behaviour in Leishmania, where the parasites change direction, speed, and tumbling behaviour in the presence of human blood-derived macrophages.

To characterise motile L. mexicana, we harvested PCF and META stages using molecularly verified culture techniques and verified each population pool via morphological distinctions and expression of stage-specific markers, Histone H4 and SHERP (de Pablos et al., 2019; Knuepfer et al., 2001; Figure 1A). We then used holographic microscopy to analyse the three-dimensional motility of suspensions of PCF and META. Subsets of this data are presented as composite renderings of 500 cell tracks in a sample volume $\sim 1.5\times 1.5\times 1.2$ mm³ (Figure 1B,C, Video 2, Video 3). In liquid culture, observed swimming speeds vary both within and between these two groups. We find the mean swimming speed of PCF cells (Figure 1D) to be 18.3 ± 0.2 μm/s (SEM), significantly lower than that of META cells (Figure 1E), which have a mean speed of 29.5 ± 0.2 μm/s (SEM). Although the mean swimming speeds are significantly different, the distributions of swimming speeds are broad and overlap: the standard deviations in swimming speed are 8.0 μm/s (PCF) and 10.4 μm/s (META). Such overlap may result from enriched, but not wholly distinct, PCF and META populations being characterised (per gene marker expression) (Figure 1A,D,E).

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Video 2

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Video 3

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Our results indicate PCF cells swim with a characteristic slow, corkscrew-like motion around a gently curving axis, a similar pattern to that of sea urchin sperm cells (Jikeli et al., 2015). In contrast, culture-derived META cells display a distinct swimming phenotype with straight path segments punctuated by sharp turning events, a ‘run and tumble’ motif reminiscent of enteric bacteria such as E. coli (Figure 1C). The ‘tumble’ events were marked by a decrease in the processive speed of the cells, consistent with a reversal of the flagellar beat and similar to pauses in motion observed in trypanosomes (Heddergott et al., 2012).

The chirality of swimming motion can be challenging to access experimentally (Rodríguez et al., 2009; Heddergott et al., 2012; Wheeler, 2017; Schuster et al., 2017), especially in cells with a complicated morphology. We assess the chirality of the tracks by using three-dimensional tracking to measure the helicity $H$ (Wilson et al., 2013) as illustrated in Figure 2A ($H=0$ implies an achiral line, identical to its mirror image). Importantly, neither PCF (Figure 2B) nor META (Figure 2C) cell tracks display a systematic chirality on the population level. Individual tracks display a right- or left-handed character, and we do not observe any switches from left- to right-handed chirality (or vice versa) in any of our tracks.

Figure 2

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To analyse the characteristic run and tumble nature of META tracks in more detail, we separate the tracks into ‘runs’ of duration ${\tau }_n=t_n-t_{n-1}$ punctuated by ‘tumbles’ through an angle θ (Figure 2D). Tumble angles appear biased towards small angles (Figure 2E), so several tumbles would typically be required to completely randomise a cell’s swimming direction. In contrast to commonly studied bacterial species that display a heuristically similar swimming pattern, the distribution of Leishmania META run durations is not a simple exponential. We observe at least two distinct run behavioural ‘regimes’, as shown in Figure 2F. Runs of $\tau <$ 1.5 s are the most common, though the distribution has a long exponential tail. Data on the longest run durations are noisy owing to the finite size of the sample volume, which limits the longest runs that may be captured. The two distinct run regimes are denoted by exponential lines of best fit (non-linear least squares regressions over the domain as indicated) in the main panel of Figure 2F, while the inset shows all data down to very low event frequencies at run durations up to 25 s. Figure 2G shows the relationship between run duration (τ) and subsequent tumble angle (θ). We note two distinct populations of events, implying two distinct processes: population 1 represents larger reorientations following shorter runs, and population 2 represents slight ‘course corrections’ during longer runs. This implies that sharper turns are suppressed during longer runs.

In bacterial systems (Berg, 1993; Turner et al., 2016), an exponential distribution of runs is the hallmark of an underlying Poisson process with a constant probability of tumbling per unit time. Fine-tuning of this tumble probability in response to a biological sensing mechanism enables cells to navigate towards or away from an external stimulus. To determine whether the behaviour of META cells was fixed or could be regulated in conditions that might favour intracellular infection, we repeated our analysis using META cells exposed to J774.2 immortalised mouse macrophages (mMφ) and human monocyte-derived macrophages (hMφ), as well as medium alone (the latter as a negative control), with results shown in Figure 3. We compare the number of cells found close to the chemotactic bait (‘Tip’) to the number found in a region 2 cm from the bait (‘Away’), as well as their respective swimming behaviour, in Figure 3A. Cells were significantly drawn toward the pipette tip in the presence of primary human blood-derived macrophages, but not to murine macrophages (p < 0.05). Unlike META cells, PCF cells showed a general attraction towards the unrefined agar in the pipette tip even in the absence of a particular stimulus (data shown in Figure 3—figure supplement 1).

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The number of tumbles exhibited by META cells decreases threefold in the presence of hMφ (Figure 3B), independent of the number of tracks observed or track length. A more detailed examination of the geometry of the tracks (quantities recapitulated in Figure 3C) yields unaltered distributions of tumble angles (Figure 3D) and run durations (Figure 3E) between control and hMφ response. This apparently contradictory observation – number of tumbles decreases, but run duration is unaffected – is explained by a sub-population retaining the unstimulated behaviour, while the majority of cells suppress tumbles almost entirely. As described in the Materials and methods section, at least two tumbles per track are required to establish tumble frequency. Therefore, if a cell performs one or zero tumbles, it will not contribute to the run duration statistics. The average displacement per run is enhanced in both ‘Tip’ and ‘Away’ cases in the presence of hMφ (Figure 3F). Importantly, this indicates the presence of a biologically relevant chemotactic stimulus from hMφ that causes META cells to swim faster and straighter.

Speed distributions for each experimental condition are shown in Figure 3G–L. There is no distinction between the ‘Tip’ and ‘Away’ speed in the presence of DMEM alone. In contrast, the presence of mMφ somewhat enriches the fraction of cells swimming at low speeds (<10 μm/s), while in the presence of hMφ the average speed is increased by 30%. These distinct phenotypic responses highlight the specificity of the parasite response, and suggest the presence of a soluble, macrophage-derived stimulus to which human-infective META are highly sensitive.

Summary and conclusions

Leishmania cells must survive changing environmental conditions throughout their life cycle, and thus motile stages vary significantly in morphology and behaviour. Pre-adaptation to the next host environment in parasitic protozoans has been well documented in trypanosomes (Hill, 2003; Rico et al., 2013). For Leishmania, this has been described in terms of changes in metabolism, gene expression (De Pablos et al., 2016), and the acquisition of a complement-resistant lipophosphoglycan coat (McConville et al., 1992; Dostálová and Volf, 2012). We have shown that META have a distinct swimming phenotype and chemotactic response compared to the precursor PCF forms.

In terms of the swimming phenotype, the similarity between L. mexicana swimming patterns and those of bacteria is superficial. Bacterial runs and tumbles are a strategy to enable navigation in an environment that randomises their swimming direction through rotational Brownian motion. Leishmania cells are at least five to eight times the (linear) size of E. coli bacteria, and rotational diffusivity is inversely proportional to particle volume, therefore Leishmania will have their orientation randomised by Brownian motion around 100 times more slowly than E. coli (Berg, 1993). It follows that Brownian motion is negligible when considering Leishmania cell orientation, and that the run and tumble behaviour observed here originates from a different imperative, serving a distinct function specific to Leishmania. More broadly, we note that foraging strategies among larger animals also follow superficially similar movement patterns (Viswanathan et al., 1999).

We find no bias in the chirality of swimming tracks in large populations. This leads us to conclude that helical swimming patterns resulting from small lateral asymmetries or irregularities in cell body shape that are sufficient to cause a rotation through asymmetric fluid drag on the cell body. This natural variation in the shape of cells can be seen in Video 1, in which a collection of META cells with beating flagella have been affixed to a glass surface using polylysine. The helical character of the tracks is more obvious in the case of PCF than in META, as seen in Figure 1F, Video 4. This is borne out in the broader histogram seen in Figure 2B compared to that in Figure 2C, and is likely a reflection of the larger cell body size relative to flagellar length, compared to META.

Video 4

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The attraction of META to hMφ is perhaps unsurprising given the biological relevance of macrophage infection in the life cycle of this parasite. Both hMφ (the definitive host cells) and neutrophils are drawn to sites of infection during inflammation, while resident dermal macrophages may act as host cells facilitating persistence (Lee et al., 2020). Chemical gradients are known to occur and persist in cutaneous infections, as damage to tissue, sandfly saliva and Leishmania-derived molecules have been shown to recruit immune cells by this mechanism (Dey et al., 2018; Giraud et al., 2018; Alcoforado Diniz et al., 2021). META cells have also been observed to move within the skin, away from their site of introduction (Peters et al., 2008). Under our assay conditions, we did not see a significant attraction to the murine macrophage line J774.2. Previous studies by others have shown that transcriptional responses to pathogens may differ significantly between primary cells and immortalised cells, for example, in the response to Mycobacterium (Andreu et al., 2017). In addition, the human macrophages used here are relatively quiescent compared to the rapidly cycling J774.2 cells, with likely altered production of metabolites that may affect chemotaxis. Further studies aimed at defining the nature of the chemotactic stimuli involved may help to clarify the observed difference in response.

The control experiments showing an attraction between PCF and DMEM-infused agar are consistent with previous studies on promastigote cultures that had not been subject to META sub-population enrichment (Oliveira et al., 2000; Leslie et al., 2002; Barros et al., 2006). We speculate that this attraction is due to the presence of small carbohydrates associated with the unrefined agar, but note that any subsequent experiments in which PCF might be exposed to hMφ would be biologically irrelevant. PCF do not naturally encounter and cannot infect hMφ, and attraction towards sugars or other inert matrix components would be difficult to decouple from attraction to cells.

It is of obvious interest to examine how more biologically relevant viscosities might impact these swimming patterns (Heddergott et al., 2012). Our in vitro analyses provide a framework to examine Leishmania spp. life cycle-dependent motility distinctions in medium, but these parasites encounter dynamic in vivo environments including sandfly bloodmeals, midgut and proboscis and mammalian dermis, bloodstream, and potentially lymph (Moll et al., 1993). In addition, T. brucei are known to sense each other’s presence and this directs movement as evidenced by in vitro social motility studies (Lopez et al., 2015; Imhof et al., 2015). While the in vivo tsetse fly environment is far more compartmentalised for T. brucei life cycle stages than that of the sandfly for Leishmania, investigating whether Leishmania parasites also display the capacity to sense each other would be an exciting and pertinent tangent.

Our studies show that flagellar motility is modified in cells adapted for infection of the human host. We show that run and tumble behaviour is a phenotypic marker of human-infective META stage parasites, and that this behaviour is suppressed in the presence of a chemotactic stimulus, causing cells to swim faster and straighter towards their target. We speculate that this provides an evolutionary advantage which promotes successful phagocytic uptake of non-replicative META cells, fundamental to human infection and parasite life cycle progression. Through optimised flagellar motility and an as-yet uncharacterised sensing mechanism, Leishmania proactively drive their uptake by human immune cells.

Data underlying the conclusions in this study are are available at the York Research Database (https://doi.org/10.15124/a8eabcd4-66c1-41a3-aa4b-423921b06568).

1. 1. Wilson LG

   (2021) The York Research Database

   Leishmania motility and chemotaxis data.

   https://doi.org/10.15124/a8eabcd4-66c1-41a3-aa4b-423921b06568

1. 1. Afzelius BA
   2. Dallai R
   3. Lanzavecchia S
   4. Bellon PL

   (1995) Flagellar structure in normal human spermatozoa and in spermatozoa that lack dynein arms

   Tissue and Cell 27:241–247.

   https://doi.org/10.1016/S0040-8166(95)80044-1

   • PubMed
   • Google Scholar
2. 1. Alcoforado Diniz J
   2. Chaves MM
   3. Vaselek S
   4. Miserani Magalhães RD
   5. Ricci-Azevedo R
   6. de Carvalho RVH
   7. Lorenzon LB
   8. Ferreira TR
   9. Zamboni D
   10. Walrad PB
   11. Volf P
   12. Sacks DL
   13. Cruz AK

   (2021) Protein methyltransferase 7 deficiency in Leishmania major increases neutrophil associated pathology in murine model

   PLOS Neglected Tropical Diseases 15:e0009230.

   https://doi.org/10.1371/journal.pntd.0009230

   • PubMed
   • Google Scholar
3. 1. Alvar J
   2. Vélez ID
   3. Bern C
   4. Herrero M
   5. Desjeux P
   6. Cano J
   7. Jannin J
   8. den Boer M
   9. WHO Leishmaniasis Control Team

   (2012) Leishmaniasis worldwide and global estimates of its incidence

   PLOS ONE 7:e35671.

   https://doi.org/10.1371/journal.pone.0035671

   • PubMed
   • Google Scholar
4. 1. Ambit A
   2. Woods KL
   3. Cull B
   4. Coombs GH
   5. Mottram JC

   (2011) Morphological events during the cell cycle of Leishmania major

   Eukaryotic Cell 10:1429–1438.

   https://doi.org/10.1128/EC.05118-11

   • PubMed
   • Google Scholar
5. 1. Andreu N
   2. Phelan J
   3. de Sessions PF
   4. Cliff JM
   5. Clark TG
   6. Hibberd ML

   (2017) Primary macrophages and J774 cells respond differently to infection with Mycobacterium tuberculosis

   Scientific Reports 7:42225.

   https://doi.org/10.1038/srep42225

   • PubMed
   • Google Scholar
6. 1. Bargul JL
   2. Jung J
   3. McOdimba FA
   4. Omogo CO
   5. Adung'a VO
   6. Krüger T
   7. Masiga DK
   8. Engstler M

   (2016) Species-Specific Adaptations of Trypanosome Morphology and Motility to the Mammalian Host

   PLOS Pathogens 12:e1005448.

   https://doi.org/10.1371/journal.ppat.1005448

   • PubMed
   • Google Scholar
7. 1. Barros VC
   2. Oliveira JS
   3. Melo MN
   4. Gontijo NF

   (2006) Leishmania amazonensis: chemotaxic and osmotaxic responses in promastigotes and their probable role in development in the phlebotomine gut

   Experimental Parasitology 112:152–157.

   https://doi.org/10.1016/j.exppara.2005.10.005

   • PubMed
   • Google Scholar
8. 1. Bates PA
   2. Rogers ME

   (2004) New insights into the developmental biology and transmission mechanisms of Leishmania

   Current molecular medicine 4:601–609.

   https://doi.org/10.2174/1566524043360285

   • PubMed
   • Google Scholar
9. 1. Beneke T
   2. Demay F
   3. Hookway E
   4. Ashman N
   5. Jeffery H
   6. Smith J
   7. Valli J
   8. Becvar T
   9. Myskova J
   10. Lestinova T
   11. Shafiq S
   12. Sadlova J
   13. Volf P
   14. Wheeler RJ
   15. Gluenz E

   (2019) Genetic dissection of a Leishmania flagellar proteome demonstrates requirement for directional motility in sand fly infections

   PLOS Pathogens 15:e1007828.

   https://doi.org/10.1371/journal.ppat.1007828

   • PubMed
   • Google Scholar
10. Book

    1. Berg HC

    (1993)

    Random Walks in Biology

    Princeton, NJ: Princeton University Press.

    • Google Scholar
11. 1. Berg HC
    2. Brown DA

    (1972) Chemotaxis in Escherichia coli analysed by three-dimensional tracking

    Nature 239:500–504.

    https://doi.org/10.1038/239500a0

    • PubMed
    • Google Scholar
12. 1. Bray RS

    (1983) Leishmania: chemotaxic responses of promastigotes and macrophages in vitro

    The Journal of Protozoology 30:322–329.

    https://doi.org/10.1111/j.1550-7408.1983.tb02923.x

    • PubMed
    • Google Scholar
13. Book

    1. Bray D

    (2001) Cell Movements: From Molecules to Motility (2nd ed)

    New York: Garland Science.

    https://doi.org/10.4324/9780203833582

    • Google Scholar
14. 1. De Pablos LM
    2. Ferreira TR
    3. Walrad PB

    (2016) Developmental differentiation in Leishmania lifecycle progression: post-transcriptional control conducts the orchestra

    Current Opinion in Microbiology 34:82–89.

    https://doi.org/10.1016/j.mib.2016.08.004

    • PubMed
    • Google Scholar
15. 1. de Pablos LM
    2. Ferreira TR
    3. Dowle AA
    4. Forrester S
    5. Parry E
    6. Newling K
    7. Walrad PB

    (2019) The mRNA-bound Proteome of Leishmania mexicana: Novel Genetic Insight into an Ancient Parasite

    Molecular & Cellular Proteomics 18:1271–1284.

    https://doi.org/10.1074/mcp.RA118.001307

    • PubMed
    • Google Scholar
16. 1. de Thomaz AA
    2. Fontes A
    3. Stahl CV
    4. Pozzo LY
    5. Ayres DC
    6. Almeida DB
    7. Farias PMA
    8. Santos BS
    9. Santos-Mallet J
    10. Gomes SAO
    11. Giorgio S
    12. Feder D
    13. Cesar CL

    (2011) Optical tweezers for studying taxis in parasites

    Journal of Optics 13:044015.

    https://doi.org/10.1088/2040-8978/13/4/044015

    • Google Scholar
17. 1. DeMarco SF
    2. Saada EA
    3. Lopez MA
    4. Hill KL

    (2020) Identification of Positive Chemotaxis in the Protozoan Pathogen Trypanosoma brucei

    mSphere 5:e00685–20.

    https://doi.org/10.1128/mSphere.00685-20

    • PubMed
    • Google Scholar
18. 1. Detke S
    2. Elsabrouty R

    (2008) Leishmania mexicana amazonensis: plasma membrane adenine nucleotide translocator and chemotaxis

    Experimental Parasitology 118:408–419.

    https://doi.org/10.1016/j.exppara.2007.10.010

    • PubMed
    • Google Scholar
19. 1. Dey R
    2. Joshi AB
    3. Oliveira F
    4. Pereira L
    5. Guimarães-Costa AB
    6. Serafim TD
    7. de Castro W
    8. Coutinho-Abreu IV
    9. Bhattacharya P
    10. Townsend S
    11. Aslan H
    12. Perkins A
    13. Karmakar S
    14. Ismail N
    15. Karetnick M
    16. Meneses C
    17. Duncan R
    18. Nakhasi HL
    19. Valenzuela JG
    20. Kamhawi S

    (2018) Gut microbes egested during bites of infected sand flies augment severity of Leishmaniasis via Inflammasome-Derived IL-1β

    Cell Host & Microbe 23:134–143.

    https://doi.org/10.1016/j.chom.2017.12.002

    • PubMed
    • Google Scholar
20. 1. Díaz E
    2. Köhidai L
    3. Ríos A
    4. Vanegas O
    5. Silva A
    6. Szabó R
    7. Mező G
    8. Hudecz F
    9. Ponte-Sucre A

    (2013) Leishmania braziliensis: cytotoxic, cytostatic and chemotactic effects of poly-lysine-methotrexate-conjugates

    Experimental Parasitology 135:134–141.

    https://doi.org/10.1016/j.exppara.2013.06.007

    • PubMed
    • Google Scholar
21. 1. Diaz E
    2. Zacarias AK
    3. Pérez S
    4. Vanegas O
    5. Köhidai L
    6. Padrón-Nieves M
    7. Ponte-Sucre A

    (2015) Effect of aliphatic, monocarboxylic, dicarboxylic, heterocyclic and sulphur-containing amino acids on Leishmania spp chemotaxis

    Parasitology 142:1621–1630.

    https://doi.org/10.1017/S003118201500116X

    • PubMed
    • Google Scholar
22. 1. Dostálová A
    2. Volf P

    (2012) Leishmania development in sand flies: parasite-vector interactions overview

    Parasites & Vectors 5:276.

    https://doi.org/10.1186/1756-3305-5-276

    • PubMed
    • Google Scholar
23. 1. Farthing NE
    2. Findlay RC
    3. Jikeli JF
    4. Walrad PB
    5. Bees MA
    6. Wilson LG

    (2017) Simultaneous two-color imaging in digital holographic microscopy

    Optics Express 25:28489–28500.

    https://doi.org/10.1364/OE.25.028489

    • PubMed
    • Google Scholar
24. 1. Gibson T
    2. Bedrossian M
    3. Serabyn E
    4. Lindensmith C
    5. Nadeau JL

    (2021) Using the Gouy phase anomaly to localize and track bacteria in digital holographic microscopy 4D images

    Journal of the Optical Society of America A 38:A11–A18.

    https://doi.org/10.1364/JOSAA.404004

    • PubMed
    • Google Scholar
25. 1. Giraud E
    2. Lestinova T
    3. Derrick T
    4. Martin O
    5. Dillon RJ
    6. Volf P
    7. Műller I
    8. Bates PA
    9. Rogers ME

    (2018) Leishmania proteophosphoglycans regurgitated from infected sand flies accelerate dermal wound repair and exacerbate leishmaniasis via insulin-like growth factor 1-dependent signalling

    PLOS Pathogens 14:e1006794.

    https://doi.org/10.1371/journal.ppat.1006794

    • Google Scholar
26. 1. Heddergott N
    2. Krüger T
    3. Babu SB
    4. Wei A
    5. Stellamanns E
    6. Uppaluri S
    7. Pfohl T
    8. Stark H
    9. Engstler M

    (2012) Trypanosome Motion Represents an Adaptation to the Crowded Environment of the Vertebrate Bloodstream

    PLOS Pathogens 8:e1003023.

    https://doi.org/10.1371/journal.ppat.1003023

    • Google Scholar
27. 1. Hill KL

    (2003) Biology and mechanism of trypanosome cell motility

    Eukaryotic Cell 2:200–208.

    https://doi.org/10.1128/EC.2.2.200-208.2003

    • PubMed
    • Google Scholar
28. 1. Imhof S
    2. Vu XL
    3. Bütikofer P
    4. Roditi I

    (2015) A glycosylation mutant of Trypanosoma brucei Links Social Motility Defects In Vitro to Impaired Colonization of Tsetse Flies In Vivo

    Eukaryotic Cell 14:588–592.

    https://doi.org/10.1128/EC.00023-15

    • PubMed
    • Google Scholar
29. 1. Jikeli JF
    2. Alvarez L
    3. Friedrich BM
    4. Wilson LG
    5. Pascal R
    6. Colin R
    7. Pichlo M
    8. Rennhack A
    9. Brenker C
    10. Kaupp UB

    (2015) Sperm navigation along helical paths in 3D chemoattractant landscapes

    Nature Communications 6:7985.

    https://doi.org/10.1038/ncomms8985

    • PubMed
    • Google Scholar
30. 1. Katz J
    2. Sheng J

    (2010) Applications of Holography in Fluid Mechanics and Particle Dynamics

    Annual Review of Fluid Mechanics 42:531–555.

    https://doi.org/10.1146/annurev-fluid-121108-145508

    • Google Scholar
31. 1. Knuepfer E
    2. Stierhof YD
    3. McKean PG
    4. Smith DF

    (2001) Characterization of a differentially expressed protein that shows an unusual localization to intracellular membranes in Leishmania major

    Biochemical Journal 356:335–344.

    https://doi.org/10.1042/bj3560335

    • PubMed
    • Google Scholar
32. 1. Kühn MJ
    2. Schmidt FK
    3. Farthing NE
    4. Rossmann FM
    5. Helm B
    6. Wilson LG
    7. Eckhardt B
    8. Thormann KM

    (2018) Spatial arrangement of several flagellins within bacterial flagella improves motility in different environments

    Nature Communications 9:5369.

    https://doi.org/10.1038/s41467-018-07802-w

    • PubMed
    • Google Scholar
33. 1. Lee SH
    2. Chaves MM
    3. Kamenyeva O
    4. Gazzinelli-Guimaraes PH
    5. Kang B
    6. Pessenda G
    7. Passelli K
    8. Tacchini-Cottier F
    9. Kabat J
    10. Jacobsen EA
    11. Nutman TB
    12. Sacks DL

    (2020) M2-like, dermal macrophages are maintained via IL-4/CCL24–mediated cooperative interaction with eosinophils in cutaneous leishmaniasis

    Science Immunology 5:eaaz4415.

    https://doi.org/10.1126/sciimmunol.aaz4415

    • Google Scholar
34. 1. Lee SH
    2. Grier DG

    (2007) Holographic microscopy of holographically trapped three-dimensional structures

    Optics Express 15:1505–1512.

    https://doi.org/10.1364/OE.15.001505

    • PubMed
    • Google Scholar
35. 1. Leslie G
    2. Barrett M
    3. Burchmore R

    (2002) Leishmania mexicana: promastigotes migrate through osmotic gradients

    Experimental Parasitology 102:117–120.

    https://doi.org/10.1016/S0014-4894(03)00031-6

    • PubMed
    • Google Scholar
36. 1. Lopez MA
    2. Saada EA
    3. Hill KL

    (2015) Insect stage-specific adenylate cyclases regulate social motility in African trypanosomes

    Eukaryotic Cell 14:104–112.

    https://doi.org/10.1128/EC.00217-14

    • PubMed
    • Google Scholar
37. 1. McConville MJ
    2. Turco SJ
    3. Ferguson MA
    4. Sacks DL

    (1992) Developmental modification of lipophosphoglycan during the differentiation of Leishmania major promastigotes to an infectious stage

    The EMBO Journal 11:3593–3600.

    https://doi.org/10.1002/j.1460-2075.1992.tb05443.x

    • PubMed
    • Google Scholar
38. 1. Molaei M
    2. Barry M
    3. Stocker R
    4. Sheng J

    (2014) Failed escape: solid surfaces prevent tumbling of Escherichia coli

    Physical Review Letters 113:068103.

    https://doi.org/10.1103/PhysRevLett.113.068103

    • PubMed
    • Google Scholar
39. 1. Moll H
    2. Fuchs H
    3. Blank C
    4. Röllinghoff M

    (1993) Langerhans cells transport Leishmania major from the infected skin to the draining lymph node for presentation to antigen-specific T cells

    European Journal of Immunology 23:1595–1601.

    https://doi.org/10.1002/eji.1830230730

    • PubMed
    • Google Scholar
40. 1. Oberholzer M
    2. Lopez MA
    3. McLelland BT
    4. Hill KL

    (2010) Social motility in african trypanosomes

    PLOS Pathogens 6:e1000739.

    https://doi.org/10.1371/journal.ppat.1000739

    • PubMed
    • Google Scholar
41. 1. Oliveira JS
    2. Melo MN
    3. Gontijo NF

    (2000) A sensitive method for assaying chemotaxic responses of Leishmania promastigotes

    Experimental Parasitology 96:187–189.

    https://doi.org/10.1006/expr.2000.4569

    • PubMed
    • Google Scholar
42. 1. Peters NC
    2. Egen JG
    3. Secundino N
    4. Debrabant A
    5. Kimblin N
    6. Kamhawi S
    7. Lawyer P
    8. Fay MP
    9. Germain RN
    10. Sacks D

    (2008) In vivo imaging reveals an essential role for neutrophils in leishmaniasis transmitted by sand flies

    Science 321:970–974.

    https://doi.org/10.1126/science.1159194

    • PubMed
    • Google Scholar
43. 1. Polin M
    2. Tuval I
    3. Drescher K
    4. Gollub JP
    5. Goldstein RE

    (2009) Chlamydomonas swims with two "gears" in a eukaryotic version of run-and-tumble locomotion

    Science 325:487–490.

    https://doi.org/10.1126/science.1172667

    • PubMed
    • Google Scholar
44. 1. Pozzo LY
    2. Fontes A
    3. de Thomaz AA
    4. Santos BS
    5. Farias PM
    6. Ayres DC
    7. Giorgio S
    8. Cesar CL

    (2009) Studying taxis in real time using optical tweezers: applications for Leishmania amazonensis parasites

    Micron 40:617–620.

    https://doi.org/10.1016/j.micron.2009.02.008

    • PubMed
    • Google Scholar
45. 1. Pulvertaft RJV
    2. Hoyle GF

    (1960) Stages in the life-cycle of Leishmania donovani

    Transactions of the Royal Society of Tropical Medicine and Hygiene 54:191–196.

    https://doi.org/10.1016/0035-9203(60)90057-2

    • Google Scholar
46. 1. Rico E
    2. Rojas F
    3. Mony BM
    4. Szoor B
    5. MacGregor P
    6. Matthews KR

    (2013) Bloodstream form pre-adaptation to the tsetse fly in Trypanosoma brucei

    Frontiers in Cellular and Infection Microbiology 3:78.

    https://doi.org/10.3389/fcimb.2013.00078

    • Google Scholar
47. 1. Rodríguez JA
    2. Lopez MA
    3. Thayer MC
    4. Zhao Y
    5. Oberholzer M
    6. Chang DD
    7. Kisalu NK
    8. Penichet ML
    9. Helguera G
    10. Bruinsma R
    11. Hill KL
    12. Miao J

    (2009) Propulsion of African trypanosomes is driven by bihelical waves with alternating chirality separated by kinks

    PNAS 106:19322–19327.

    https://doi.org/10.1073/pnas.0907001106

    • PubMed
    • Google Scholar
48. 1. Rogers M
    2. Kropf P
    3. Choi BS
    4. Dillon R
    5. Podinovskaia M
    6. Bates P
    7. Müller I

    (2009) Proteophosophoglycans regurgitated by Leishmania-infected sand flies target the L-arginine metabolism of host macrophages to promote parasite survival

    PLOS Pathogens 5:e1000555.

    https://doi.org/10.1371/journal.ppat.1000555

    • PubMed
    • Google Scholar
49. 1. Romero GG
    2. Monaldi AC
    3. Alanís EE

    (2012) Digital holographic microscopy for detection of Trypanosoma cruzi parasites in fresh blood mounts

    Optics Communications 285:1613–1618.

    https://doi.org/10.1016/j.optcom.2011.11.077

    • Google Scholar
50. 1. Sacks DL
    2. Perkins PV

    (1984) Identification of an infective stage of Leishmania promastigotes

    Science 223:1417–1419.

    https://doi.org/10.1126/science.6701528

    • PubMed
    • Google Scholar
51. 1. Schuster S
    2. Krüger T
    3. Subota I
    4. Thusek S
    5. Rotureau B
    6. Beilhack A
    7. Engstler M

    (2017) Developmental adaptations of trypanosome motility to the tsetse fly host environments unravel a multifaceted in vivo microswimmer system

    eLife 6:e27656.

    https://doi.org/10.7554/eLife.27656

    • PubMed
    • Google Scholar
52. 1. Shaw S
    2. DeMarco SF
    3. Rehmann R
    4. Wenzler T
    5. Florini F
    6. Roditi I
    7. Hill KL

    (2019) Flagellar cAMP signaling controls trypanosome progression through host tissues

    Nature Communications 10:803.

    https://doi.org/10.1038/s41467-019-08696-y

    • PubMed
    • Google Scholar
53. 1. Singh DP
    2. Uspal WE
    3. Popescu MN
    4. Wilson LG
    5. Fischer P

    (2018) Photogravitactic Microswimmers

    Advanced Functional Materials 28:1706660.

    https://doi.org/10.1002/adfm.201706660

    • Google Scholar
54. 1. Son K
    2. Menolascina F
    3. Stocker R

    (2016) Speed-dependent chemotactic precision in marine bacteria

    PNAS 113:8624–8629.

    https://doi.org/10.1073/pnas.1602307113

    • PubMed
    • Google Scholar
55. 1. Späth GF
    2. Beverley SM

    (2001) A lipophosphoglycan-independent method for isolation of infective Leishmania metacyclic promastigotes by density gradient centrifugation

    Experimental Parasitology 99:97–103.

    https://doi.org/10.1006/expr.2001.4656

    • PubMed
    • Google Scholar
56. 1. Taktikos J
    2. Stark H
    3. Zaburdaev V

    (2013) How the motility pattern of bacteria affects their dispersal and chemotaxis

    PLOS ONE 8:e81936.

    https://doi.org/10.1371/journal.pone.0081936

    • PubMed
    • Google Scholar
57. 1. Thornton KL
    2. Butler JK
    3. Davis SJ
    4. Baxter BK
    5. Wilson LG

    (2020) Haloarchaea swim slowly for optimal chemotactic efficiency in low nutrient environments

    Nature Communications 11:4453.

    https://doi.org/10.1038/s41467-020-18253-7

    • PubMed
    • Google Scholar
58. 1. Turner L
    2. Ping L
    3. Neubauer M
    4. Berg HC

    (2016) Visualizing Flagella while Tracking Bacteria

    Biophysical Journal 111:630–639.

    https://doi.org/10.1016/j.bpj.2016.05.053

    • PubMed
    • Google Scholar
59. 1. Viswanathan GM
    2. Buldyrev SV
    3. Havlin S
    4. da Luz MG
    5. Raposo EP
    6. Stanley HE

    (1999) Optimizing the success of random searches

    Nature 401:911–914.

    https://doi.org/10.1038/44831

    • PubMed
    • Google Scholar
60. 1. Weiße S
    2. Heddergott N
    3. Heydt M
    4. Pflästerer D
    5. Maier T
    6. Haraszti T
    7. Grunze M
    8. Engstler M
    9. Rosenhahn A

    (2020) A Quantitative 3D Motility Analysis of Trypanosoma brucei by Use of Digital In-line Holographic Microscopy

    PLOS ONE 7:e37296.

    https://doi.org/10.1371/journal.pone.0037296

    • Google Scholar
61. 1. Wheeler RJ

    (2017) Use of chiral cell shape to ensure highly directional swimming in trypanosomes

    PLOS Computational Biology 13:e1005353.

    https://doi.org/10.1371/journal.pcbi.1005353

    • PubMed
    • Google Scholar
62. 1. Wilson LG
    2. Carter LM
    3. Reece SE

    (2013) High-speed holographic microscopy of malaria parasites reveals ambidextrous flagellar waveforms

    PNAS 110:18769–18774.

    https://doi.org/10.1073/pnas.1309934110

    • PubMed
    • Google Scholar
63. 1. Wilson L
    2. Zhang R

    (2012) 3D Localization of weak scatterers in digital holographic microscopy using Rayleigh-Sommerfeld back-propagation

    Optics Express 20:16735–16744.

    https://doi.org/10.1364/OE.20.016735

    • Google Scholar
64. 1. Zhang Y
    2. Ceylan Koydemir H
    3. Shimogawa MM
    4. Yalcin S
    5. Guziak A
    6. Liu T
    7. Oguz I
    8. Huang Y
    9. Bai B
    10. Luo Y
    11. Luo Y
    12. Wei Z
    13. Wang H
    14. Bianco V
    15. Zhang B
    16. Nadkarni R
    17. Hill K
    18. Ozcan A

    (2018) Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning

    Light: Science & Applications 7:108.

    https://doi.org/10.1038/s41377-018-0110-1

    • PubMed
    • Google Scholar

Author details

1. Rachel C Findlay

   1. York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
   2. Department of Physics, University of York, York, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3235-130X

2. Mohamed Osman

   York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

3. Kirstin A Spence

   York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom

   Contribution

   Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1220-4698

4. Paul M Kaye

   York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom

   Contribution

   Resources, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8796-4755

5. Pegine B Walrad

   York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   pegine.walrad@york.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2302-0720

6. Laurence G Wilson

   Department of Physics, University of York, York, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   laurence.wilson@york.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6659-6001

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